diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml deleted file mode 100644 index e46e5619..00000000 --- a/.gitlab-ci.yml +++ /dev/null @@ -1,37 +0,0 @@ -# This file is a template, and might need editing before it works on your project. -# Official language image. Look for the different tagged releases at: -# https://hub.docker.com/r/library/rust/tags/ -image: "rust:1.54" - -# Use cargo to test the project -test:format: - script: - - rustup component add rustfmt - - cargo fmt --version && cargo --version # Print version info for debugging - - cargo fmt --all -- --check --verbose - -# Use cargo to test the project -test:cargo: - script: - - rustc --version && cargo --version # Print version info for debugging - - cargo test --workspace --all-targets --all-features --verbose - -# Use cargo to test the project -test:clippy: - script: - - rustup component add clippy - - cargo clippy --version && cargo --version # Print version info for debugging - - cargo clippy --workspace --all-targets --all-features -- -D warnings - -# Build and host the documentation -# pages: -# stage: deploy -# script: -# - rustdoc --version && cargo --version -# - cargo doc -# - mv target/doc public -# artifacts: -# paths: -# - public -# only: -# - master diff --git a/Cargo.toml b/Cargo.toml index 2c433239..37bbe69c 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -1,40 +1,34 @@ [package] -authors = ["Leopold Luley "] -edition = "2021" name = "mahf" version = "0.1.0" +authors = ["Leopold Luley ", "Helena Stegherr ", "Jonathan Wurth "] +edition = "2021" description = "A framework for modular construction and evaluation of metaheuristics." +readme = "README.md" license = "GPL-3.0-or-later" repository = "https://github.com/mahf-opt/mahf" keywords = ["heuristic", "metaheuristic", "optimization"] categories = ["science", "algorithms"] [dependencies] -anyhow = "1.0.51" -ciborium = "0.2.0" -coco-rs = "0.6" -derive_more = { version = "0.99.17", features = ["deref", "deref_mut"]} -embed-doc-image = "0.1.4" -erased-serde = "0.3.16" -float_eq = "0.7.0" -num_cpus = "1.13.0" -pest = "2.1.3" -pest_consume = "1.1.1" -pest_derive = "2.1.0" -rand = "0.8.4" -rand_distr = "0.4.2" -ron = "0.7.0" -rprompt = "1.0.5" -serde = {version = "1.0.131", features = ["derive"]} +better_any = { version = "0.2.0", features = ["derive"] } +thiserror = "1.0.40" +eyre = "0.6.8" +color-eyre = "0.6.2" trait-set = "0.3.0" -dyn-clone = "1.0.9" +serde = { version = "1.0.160", features = ["derive"] } +erased-serde = "0.3.25" +derive_more = { version = "0.99.17", features = ["deref", "deref_mut", "add", "mul", "not"] } +rand = "0.8.5" +rand_distr = "0.4.3" +dyn-clone = "1.0.11" derivative = "2.2.0" +ciborium = "0.2.0" +serde_json = "1.0.96" +rayon = "1.7.0" +test-case = "3.1.0" +float_eq = "1.0.1" +contracts = "0.6.3" itertools = "0.10.5" -better_any = { version = "0.2.0", features = ["derive"] } -scoped_threadpool = "0.1.9" - -[dev-dependencies] -proptest = "1.0.0" - -[build-dependencies] -cc = "1.0.72" +ron = "0.8.0" +indicatif = { version = "0.17.4", features = ["rayon"] } \ No newline at end of file diff --git a/README.md b/README.md index eace9e10..eda24e03 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,11 @@ # MAHF -A framework for modular construction and evaluation of meta-heuristics. +A framework for modular construction and evaluation of metaheuristics. # Purpose and Features -MAHF aims to make construction and modification of metaheuristics as simple and reliable as possible. In addition to construction it also provides utilities for tracking, evaluation and comparison of those heuristics. +MAHF aims to make construction and modification of metaheuristics as simple and reliable as possible. In addition to +construction it also provides utilities for tracking, evaluation and comparison of those heuristics. - Simple modular construction of metaheuristics - State management and state tracking @@ -32,21 +33,42 @@ MAHF aims to make construction and modification of metaheuristics as simple and MAHF has extensive documentation which should make it easy to get started. Just run + ```sh $ cargo doc --open ``` + to build and open the documentation. # Examples Examples on how to use MAHF for evaluation can be found in the [examples](examples) directory. -Examples of heuristics can be found under [heuristics](src/heuristics) and components under [components](src/components). +Examples of heuristics can be found under [heuristics](src/heuristics) and components +under [components](src/components). # Additional Resources None yet. -# Papers and Projects using MAHF +# Publications -None yet. +If you use MAHF in a scientific publication, we would appreciate citations to the following paper: + +Helena Stegherr, Leopold Luley, Jonathan Wurth, Michael Heider, and Jörg Hähner. 2023. A framework for modular +construction and evaluation of metaheuristics. Fakultät für Angewandte +Informatik. https://opus.bibliothek.uni-augsburg.de/opus4/103452 + +Bibtex entry: + +```bibtex +@techreport{stegherr2023, + author = {Helena Stegherr and Leopold Luley and Jonathan Wurth and Michael Heider and J{\"o}rg H{\"a}hner}, + title = {A framework for modular construction and evaluation of metaheuristics}, + institution = {Fakult{\"a}t f{\"u}r Angewandte Informatik}, + series = {Reports / Technische Berichte der Fakult{\"a}t f{\"u}r Angewandte Informatik der Universit{\"a}t Augsburg}, + number = {2023-01}, + pages = {25}, + year = {2023}, +} +``` \ No newline at end of file diff --git a/build.rs b/build.rs deleted file mode 100644 index 6818ef2c..00000000 --- a/build.rs +++ /dev/null @@ -1,5 +0,0 @@ -fn main() { - cc::Build::new() - .file("src/problems/coco/suits/bbob/legacy_code.c") - .compile("bbob_legacy"); -} diff --git a/examples/bmf.rs b/examples/bmf.rs deleted file mode 100644 index 69cca5bc..00000000 --- a/examples/bmf.rs +++ /dev/null @@ -1,29 +0,0 @@ -use mahf::prelude::*; -use problems::bmf::BenchmarkFunction; - -fn main() { - // Specify the problem: Sphere function with 10 dimensions. - let problem: BenchmarkFunction = BenchmarkFunction::sphere(/*dim: */ 10); - // Specify the metaheuristic: Particle Swarm Optimization (pre-implemented in MAHF). - let config: Configuration = pso::real_pso( - /*params: */ - pso::RealProblemParameters { - num_particles: 20, - weight: 1.0, - c_one: 1.0, - c_two: 1.0, - v_max: 1.0, - }, - /*termination: */ - termination::FixedIterations::new(/*max_iterations: */ 500) - & termination::DistanceToOpt::new(0.01), - ); - - // Execute the metaheuristic on the problem with a random seed. - let state: State = config.optimize(&problem); - - // Print the results. - println!("Found Individual: {:?}", state.best_individual().unwrap()); - println!("This took {} iterations.", state.iterations()); - println!("Global Optimum: {}", problem.known_optimum()); -} diff --git a/examples/coco.rs b/examples/coco.rs deleted file mode 100644 index 2153e93c..00000000 --- a/examples/coco.rs +++ /dev/null @@ -1,32 +0,0 @@ -use mahf::{prelude::*, state::common, tracking::LogSet}; -use problems::coco_bound::{suits, CocoInstance}; -use tracking::{functions, trigger}; - -fn main() -> anyhow::Result<()> { - let output = "data/coco/iwo"; - let config = iwo::real_iwo( - iwo::RealProblemParameters { - initial_population_size: 5, - max_population_size: 20, - min_number_of_seeds: 0, - max_number_of_seeds: 5, - initial_deviation: 0.5, - final_deviation: 0.001, - modulation_index: 3, - }, - termination::FixedIterations::new(500) & termination::TargetHit::new(), - ); - let suite = suits::bbob(); - - suits::evaluate_suite(suite, config, output, |state| { - state.insert( - LogSet::::new() - .with_common_extractors(trigger::Iteration::new(10)) - .with( - trigger::Change::::new(0.1), - functions::auto::, - ) - .with(trigger::Iteration::new(50), functions::best_individual), - ) - }) -} diff --git a/examples/sphere.rs b/examples/sphere.rs new file mode 100644 index 00000000..1cd0a56c --- /dev/null +++ b/examples/sphere.rs @@ -0,0 +1,81 @@ +use mahf::prelude::*; +use mahf::problems::KnownOptimumProblem; +use mahf::SingleObjective; +use std::ops::Range; + +pub struct Sphere { + pub dim: usize, +} + +impl Sphere { + pub fn new(dim: usize) -> Self { + Self { dim } + } +} + +impl problems::Problem for Sphere { + type Encoding = Vec; + type Objective = SingleObjective; + + fn name(&self) -> &str { + "Sphere" + } +} + +impl problems::VectorProblem for Sphere { + type Element = f64; + + fn dimension(&self) -> usize { + self.dim + } +} + +impl problems::LimitedVectorProblem for Sphere { + fn domain(&self) -> Vec> { + std::iter::repeat(-1.0..1.0).take(self.dim).collect() + } +} + +impl problems::ObjectiveFunction for Sphere { + fn objective(solution: &Self::Encoding) -> Self::Objective { + solution + .iter() + .map(|x| x.powi(2)) + .sum::() + .try_into() + .unwrap() + } +} + +impl KnownOptimumProblem for Sphere { + fn known_optimum(&self) -> SingleObjective { + 0.0.try_into().unwrap() + } +} + +fn main() { + // Specify the problem: Sphere function with 10 dimensions. + let problem: Sphere = Sphere::new(/*dim: */ 10); + // Specify the metaheuristic: Particle Swarm Optimization (pre-implemented in MAHF). + let config: Configuration = pso::real_pso( + /*params: */ + pso::RealProblemParameters { + num_particles: 20, + weight: 1.0, + c_one: 1.0, + c_two: 1.0, + v_max: 1.0, + }, + /*termination: */ + termination::FixedIterations::new(/*max_iterations: */ 500) + & termination::DistanceToOpt::new(0.01), + ); + + // Execute the metaheuristic on the problem with a random seed. + let state: State = config.optimize(&problem); + + // Print the results. + println!("Found Individual: {:?}", state.best_individual().unwrap()); + println!("This took {} iterations.", state.iterations()); + println!("Global Optimum: {:?}", problem.known_optimum()); +} diff --git a/examples/tsp.rs b/examples/tsp.rs deleted file mode 100644 index 28291b00..00000000 --- a/examples/tsp.rs +++ /dev/null @@ -1,43 +0,0 @@ -use aco::ant_ops; -use mahf::prelude::*; -use problems::tsp::{self, SymmetricTsp}; -use tracking::{files, functions, trigger}; - -fn main() { - // Specify the problem: TSPLIB instance Berlin52. - let problem: SymmetricTsp = tsp::Instances::BERLIN52.load(); - // Specify the metaheuristic: Ant System. - let config: Configuration = Configuration::builder() - .do_(initialization::Empty::new()) - .while_( - termination::FixedEvaluations::new(/*max_evaluations: */ 10_000), - |builder| { - builder - .do_(ant_ops::AcoGeneration::new( - /*num_ants: */ 20, /*alpha: */ 2.0, /*beta: */ 1.0, - /*initial_pheromones: */ 0.0, - )) - .evaluate() - .update_best_individual() - .do_(ant_ops::AsPheromoneUpdate::new( - /*evaporation: */ 0.2, /*decay_coefficient: */ 1.0, - )) - .do_(tracking::Logger::new()) - }, - ) - .build(); - - // Execute the metaheuristic on the problem. - let state: State = config.optimize_with(&problem, |state| { - // Set the seed to 42. - state.insert(Random::seeded(42)); - // Log the best individual every 50 iterations. - state.insert( - tracking::LogSet::::new() - .with(trigger::Iteration::new(50), functions::best_individual), - ); - }); - - // Save the log to file "aco_berlin52.log". - files::write_log_file("aco_berlin52.log", state.log()).unwrap(); -} diff --git a/src/components/constraints.rs b/src/components/constraints.rs index 7466038c..31f59f14 100644 --- a/src/components/constraints.rs +++ b/src/components/constraints.rs @@ -39,18 +39,18 @@ where pub struct Saturation; impl Saturation { - pub fn new> + VectorProblem + LimitedVectorProblem>( - ) -> Box> { + pub fn new< + P: Problem> + VectorProblem + LimitedVectorProblem, + >() -> Box> { Box::new(BoundaryConstrainer(Self)) } } -impl> + VectorProblem + LimitedVectorProblem> +impl> + VectorProblem + LimitedVectorProblem> BoundaryConstraint

for Saturation { fn constrain(&self, solution: &mut Vec, problem: &P, _state: &mut State

) { - for (d, x) in solution.iter_mut().enumerate() { - let range = problem.range(d); + for (x, range) in solution.iter_mut().zip(problem.domain()) { *x = x.clamp(range.start, range.end); } } @@ -62,18 +62,18 @@ impl> + VectorProblem + LimitedVectorPro pub struct Toroidal; impl Toroidal { - pub fn new> + VectorProblem + LimitedVectorProblem>( - ) -> Box> { + pub fn new< + P: Problem> + VectorProblem + LimitedVectorProblem, + >() -> Box> { Box::new(BoundaryConstrainer(Self)) } } -impl> + VectorProblem + LimitedVectorProblem> +impl> + VectorProblem + LimitedVectorProblem> BoundaryConstraint

for Toroidal { fn constrain(&self, solution: &mut Vec, problem: &P, _state: &mut State

) { - for (d, x) in solution.iter_mut().enumerate() { - let range = problem.range(d); + for (x, range) in solution.iter_mut().zip(problem.domain()) { let a = range.start; let b = range.end; let d = b - a; @@ -93,18 +93,18 @@ impl> + VectorProblem + LimitedVectorPro pub struct Mirror; impl Mirror { - pub fn new> + VectorProblem + LimitedVectorProblem>( - ) -> Box> { + pub fn new< + P: Problem> + VectorProblem + LimitedVectorProblem, + >() -> Box> { Box::new(BoundaryConstrainer(Self)) } } -impl> + VectorProblem + LimitedVectorProblem> +impl> + VectorProblem + LimitedVectorProblem> BoundaryConstraint

for Mirror { fn constrain(&self, solution: &mut Vec, problem: &P, _state: &mut State

) { - for (d, x) in solution.iter_mut().enumerate() { - let range = problem.range(d); + for (x, range) in solution.iter_mut().zip(problem.domain()) { let a = range.start; let b = range.end; @@ -131,18 +131,18 @@ impl> + VectorProblem + LimitedVectorPro pub struct CompleteOneTailedNormalCorrection; impl CompleteOneTailedNormalCorrection { - pub fn new> + VectorProblem + LimitedVectorProblem>( - ) -> Box> { + pub fn new< + P: Problem> + VectorProblem + LimitedVectorProblem, + >() -> Box> { Box::new(BoundaryConstrainer(Self)) } } -impl> + VectorProblem + LimitedVectorProblem> +impl> + VectorProblem + LimitedVectorProblem> BoundaryConstraint

for CompleteOneTailedNormalCorrection { fn constrain(&self, solution: &mut Vec, problem: &P, state: &mut State

) { - for (d, x) in solution.iter_mut().enumerate() { - let range = problem.range(d); + for (x, range) in solution.iter_mut().zip(problem.domain()) { let a = range.start; let b = range.end; diff --git a/src/components/evaluation.rs b/src/components/evaluation.rs index 9b56684b..1ec1ef95 100644 --- a/src/components/evaluation.rs +++ b/src/components/evaluation.rs @@ -32,6 +32,7 @@ impl Component

for Evaluator { state.insert(common::Evaluations(0)); if !state.has::>() { + #[allow(deprecated)] state.insert(problem.default_evaluator()); } } diff --git a/src/components/generation/mod.rs b/src/components/generation/mod.rs index 7e90f51c..0b048a1d 100644 --- a/src/components/generation/mod.rs +++ b/src/components/generation/mod.rs @@ -23,7 +23,7 @@ impl RandomPermutation { /// Creates this component as an generator, modifying the current population. pub fn new_gen

() -> Box> where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { Box::new(Generator(Self { initial_population_size: None, @@ -32,7 +32,7 @@ impl RandomPermutation { } impl

Generation

for RandomPermutation where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { fn generate_population( &self, @@ -51,7 +51,7 @@ impl RandomSpread { pub fn new_gen() -> Box> where D: SampleUniform + Clone + PartialOrd + 'static, - P: Problem> + LimitedVectorProblem, + P: Problem> + LimitedVectorProblem, { Box::new(Generator(Self { initial_population_size: None, @@ -61,7 +61,7 @@ impl RandomSpread { impl Generation

for RandomSpread where D: SampleUniform + Clone + PartialOrd + 'static, - P: Problem> + LimitedVectorProblem, + P: Problem> + LimitedVectorProblem, { fn generate_population( &self, @@ -81,7 +81,7 @@ impl RandomBitstring { /// Creates this component as an generator, modifying the current population. pub fn new_gen

(p: f64) -> Box> where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { Box::new(Generator(Self { initial_population_size: None, @@ -94,7 +94,7 @@ impl RandomBitstring { /// Creates this component as an generator, modifying the current population. pub fn new_uniform_gen

() -> Box> where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { Box::new(Generator(Self { initial_population_size: None, @@ -104,7 +104,7 @@ impl RandomBitstring { } impl

Generation

for RandomBitstring where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { fn generate_population( &self, diff --git a/src/components/generation/mutation.rs b/src/components/generation/mutation.rs index aacff871..71499a53 100644 --- a/src/components/generation/mutation.rs +++ b/src/components/generation/mutation.rs @@ -187,7 +187,7 @@ pub struct UniformMutation { impl UniformMutation { pub fn new

(rm: f64) -> Box> where - P: Problem> + LimitedVectorProblem, + P: Problem> + LimitedVectorProblem, { Box::new(Generator(Self { rm })) } @@ -195,7 +195,7 @@ impl UniformMutation { impl

Generation

for UniformMutation where - P: Problem> + LimitedVectorProblem, + P: Problem> + LimitedVectorProblem, { fn generate_population( &self, @@ -205,41 +205,16 @@ where ) { let rng = state.random_mut(); - for solution in population.iter_mut() { + for (solution, range) in population.iter_mut().zip(problem.domain()) { for x in solution.iter_mut() { if rng.gen_bool(self.rm) { - *x = rng.gen_range(problem.range(problem.dimension())); + *x = rng.gen_range(range.clone()); } } } } } -#[cfg(test)] -mod uniform_mutation { - use crate::problems::bmf::BenchmarkFunction; - use crate::state::Random; - - use super::*; - - #[test] - fn all_mutated() { - let problem = BenchmarkFunction::sphere(3); - let comp = UniformMutation { rm: 1.0 }; - let mut state = State::new(); - state.insert(Random::testing()); - let mut population = vec![vec![0.1, 0.2, 0.4], vec![0.2, 0.3, 0.6]]; - let parents_length = population.len(); - let solution_length = vec![population[0].len(), population[1].len()]; - comp.generate_population(&mut population, &problem, &mut state); - assert_eq!(population.len(), parents_length); - assert_eq!( - vec![population[0].len(), population[1].len()], - solution_length - ); - } -} - /// Applies a gaussian mutation to each position depending on mutation rate. /// The distribution is centered around 0 and the resulting value is added to the value of the solution. /// @@ -286,34 +261,6 @@ where } } -#[cfg(test)] -mod gaussian_mutation { - use crate::problems::bmf::BenchmarkFunction; - use crate::state::Random; - - use super::*; - - #[test] - fn all_mutated() { - let problem = BenchmarkFunction::sphere(3); - let comp = GaussianMutation { - rm: 1.0, - deviation: 0.1, - }; - let mut state = State::new(); - state.insert(Random::testing()); - let mut population = vec![vec![0.1, 0.2, 0.4], vec![0.2, 0.3, 0.6]]; - let parents_length = population.len(); - let solution_length = vec![population[0].len(), population[1].len()]; - comp.generate_population(&mut population, &problem, &mut state); - assert_eq!(population.len(), parents_length); - assert_eq!( - vec![population[0].len(), population[1].len()], - solution_length - ); - } -} - /// Applies a bitflip mutation to each position depending on mutation rate. /// /// Only for binary encodings! @@ -405,31 +352,6 @@ where } } -#[cfg(test)] -mod swap_mutation { - use crate::problems::bmf::BenchmarkFunction; - use crate::state::Random; - - use super::*; - - #[test] - fn all_mutated() { - let problem = BenchmarkFunction::sphere(3); - let comp = SwapMutation { n_swap: 2 }; - let mut state = State::new(); - state.insert(Random::testing()); - let mut population = vec![vec![0.1, 0.2, 0.4, 0.5, 0.9], vec![0.2, 0.3, 0.6, 0.7, 0.8]]; - let parents_length = population.len(); - let solution_length = vec![population[0].len(), population[1].len()]; - comp.generate_population(&mut population, &problem, &mut state); - assert_eq!(population.len(), parents_length); - assert_eq!( - vec![population[0].len(), population[1].len()], - solution_length - ); - } -} - /// Applies a scramble mutation to the solution depending on mutation probability. /// /// Shuffles the solution. @@ -465,31 +387,6 @@ where } } -#[cfg(test)] -mod scramble_mutation { - use crate::problems::bmf::BenchmarkFunction; - use crate::state::Random; - - use super::*; - - #[test] - fn all_mutated() { - let problem = BenchmarkFunction::sphere(3); - let comp = ScrambleMutation; - let mut state = State::new(); - state.insert(Random::testing()); - let mut population = vec![vec![0.1, 0.2, 0.4, 0.5, 0.9], vec![0.2, 0.3, 0.6, 0.7, 0.8]]; - let parents_length = population.len(); - let solution_length = vec![population[0].len(), population[1].len()]; - comp.generate_population(&mut population, &problem, &mut state); - assert_eq!(population.len(), parents_length); - assert_eq!( - vec![population[0].len(), population[1].len()], - solution_length - ); - } -} - /// Applies a insertion mutation to the solution depending on mutation probability. /// /// Removes one random element of the solution and inserts it on a random position. @@ -526,31 +423,6 @@ where } } -#[cfg(test)] -mod insertion_mutation { - use crate::problems::bmf::BenchmarkFunction; - use crate::state::Random; - - use super::*; - - #[test] - fn all_mutated() { - let problem = BenchmarkFunction::sphere(3); - let comp = InsertionMutation; - let mut state = State::new(); - state.insert(Random::testing()); - let mut population = vec![vec![0.1, 0.2, 0.4, 0.5, 0.9], vec![0.2, 0.3, 0.6, 0.7, 0.8]]; - let parents_length = population.len(); - let solution_length = vec![population[0].len(), population[1].len()]; - comp.generate_population(&mut population, &problem, &mut state); - assert_eq!(population.len(), parents_length); - assert_eq!( - vec![population[0].len(), population[1].len()], - solution_length - ); - } -} - /// Applies a inversion mutation to the solution depending on mutation probability. /// /// Takes a random slice of the solution and inverts it. @@ -590,31 +462,6 @@ where } } -#[cfg(test)] -mod inversion_mutation { - use crate::problems::bmf::BenchmarkFunction; - use crate::state::Random; - - use super::*; - - #[test] - fn all_mutated() { - let problem = BenchmarkFunction::sphere(3); - let comp = InversionMutation; - let mut state = State::new(); - state.insert(Random::testing()); - let mut population = vec![vec![0.1, 0.2, 0.4, 0.5, 0.9], vec![0.2, 0.3, 0.6, 0.7, 0.8]]; - let parents_length = population.len(); - let solution_length = vec![population[0].len(), population[1].len()]; - comp.generate_population(&mut population, &problem, &mut state); - assert_eq!(population.len(), parents_length); - assert_eq!( - vec![population[0].len(), population[1].len()], - solution_length - ); - } -} - /// Applies a translocation mutation to the solution depending on mutation probability. /// /// Takes a random slice of the solution and inserts it at a new position. @@ -663,31 +510,6 @@ where } } -#[cfg(test)] -mod translocation_mutation { - use crate::problems::bmf::BenchmarkFunction; - use crate::state::Random; - - use super::*; - - #[test] - fn all_mutated() { - let problem = BenchmarkFunction::sphere(3); - let comp = TranslocationMutation; - let mut state = State::new(); - state.insert(Random::testing()); - let mut population = vec![vec![0.1, 0.2, 0.4, 0.5, 0.9], vec![0.2, 0.3, 0.6, 0.7, 0.8]]; - let parents_length = population.len(); - let solution_length = vec![population[0].len(), population[1].len()]; - comp.generate_population(&mut population, &problem, &mut state); - assert_eq!(population.len(), parents_length); - assert_eq!( - vec![population[0].len(), population[1].len()], - solution_length - ); - } -} - /// Performs the special Differential Evolution mutation, similar to an arithmetic crossover. /// /// Requires a DE selection directly beforehand, e.g., [DEBest][crate::components::selection::DEBest]. @@ -700,7 +522,7 @@ pub struct DEMutation { } impl DEMutation { - pub fn new> + VectorProblem>( + pub fn new>( y: usize, f: f64, ) -> Box> { @@ -712,7 +534,7 @@ impl DEMutation { impl

Generation

for DEMutation where - P: Problem> + VectorProblem, + P: Problem + VectorProblem, { fn generate_population( &self, @@ -755,31 +577,6 @@ where } } -#[cfg(test)] -mod de_mutation { - use crate::problems::bmf::BenchmarkFunction; - use crate::state::Random; - - use super::*; - - #[test] - fn all_mutated() { - let problem = BenchmarkFunction::sphere(3); - let y = 1; - let comp = DEMutation { y, f: 1. }; - let mut state = State::new(); - state.insert(Random::testing()); - let mut population = vec![ - vec![0.1, 0.2, 0.4, 0.5, 0.9], - vec![0.2, 0.3, 0.6, 0.7, 0.8], - vec![0.1, 0.3, 0.5, 0.7, 0.9], - ]; - let parents_length = population.len(); - comp.generate_population(&mut population, &problem, &mut state); - assert_eq!(population.len() * (2 * y + 1), parents_length); - } -} - /// Applies a mutation only on some uniformly sampled dimensions of the solution. #[derive(Serialize, derivative::Derivative)] #[serde(bound = "")] diff --git a/src/components/generation/recombination.rs b/src/components/generation/recombination.rs index e4cac2d2..5bf3c1b2 100644 --- a/src/components/generation/recombination.rs +++ b/src/components/generation/recombination.rs @@ -151,35 +151,6 @@ where } } -#[cfg(test)] -mod npoint_crossover { - use crate::problems::bmf::BenchmarkFunction; - use crate::state::Random; - - use super::*; - - #[test] - fn all_recombined() { - let problem = BenchmarkFunction::sphere(3); - let comp = NPointCrossover { - pc: 1.0, - points: 3, - keep_both: true, - }; - let mut state = State::new(); - state.insert(Random::testing()); - let population = vec![ - vec![0.1, 0.2, 0.4, 0.5, 0.9], - vec![0.2, 0.3, 0.6, 0.7, 0.8], - vec![0.11, 0.21, 0.41, 0.51, 0.91], - ]; - let parents_length = population.len(); - let mut offspring = Vec::new(); - comp.recombine_solutions(population, &mut offspring, &problem, &mut state); - assert_eq!(offspring.len(), parents_length); - } -} - /// Applies a uniform crossover to two parent solutions depending on crossover probability. /// /// If pc = 1, the solutions are recombined. @@ -258,34 +229,6 @@ where } } -#[cfg(test)] -mod uniform_crossover { - use crate::problems::bmf::BenchmarkFunction; - use crate::state::Random; - - use super::*; - - #[test] - fn all_recombined() { - let problem = BenchmarkFunction::sphere(3); - let comp = UniformCrossover { - pc: 1.0, - keep_both: true, - }; - let mut state = State::new(); - state.insert(Random::testing()); - let population = vec![ - vec![0.1, 0.2, 0.4, 0.5, 0.9], - vec![0.2, 0.3, 0.6, 0.7, 0.8], - vec![0.11, 0.21, 0.41, 0.51, 0.91], - ]; - let parents_length = population.len(); - let mut offspring = Vec::new(); - comp.recombine_solutions(population, &mut offspring, &problem, &mut state); - assert_eq!(offspring.len(), parents_length); - } -} - /// Applies a cycle crossover to two parent solutions depending on crossover probability. /// /// Usually exclusive to combinatorial problems. @@ -360,30 +303,6 @@ where } } -#[cfg(test)] -mod cycle_crossover { - use crate::problems::bmf::BenchmarkFunction; - use crate::state::Random; - - use super::*; - - #[test] - fn all_recombined() { - let problem = BenchmarkFunction::sphere(3); - let comp = CycleCrossover { pc: 1.0 }; - let mut state = State::new(); - state.insert(Random::testing()); - let population = vec![ - vec![8.0, 4.0, 7.0, 3.0, 6.0, 2.0, 5.0, 1.0, 9.0, 0.0], - vec![0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], - ]; - let parents_length = population.len(); - let mut offspring = Vec::new(); - comp.recombine_solutions(population, &mut offspring, &problem, &mut state); - assert_eq!(offspring.len(), parents_length); - } -} - /// Performs a binomial crossover, combining two individuals from two populations at the same index. /// /// Requires at least two populations on the stack, where the top population is modified. @@ -394,14 +313,14 @@ pub struct DEBinomialCrossover { pc: f64, } impl DEBinomialCrossover { - pub fn new> + VectorProblem>(pc: f64) -> Box> { + pub fn new>(pc: f64) -> Box> { Box::new(Self { pc }) } } impl

Component

for DEBinomialCrossover where - P: Problem> + VectorProblem, + P: Problem + VectorProblem, { fn execute(&self, problem: &P, state: &mut State

) { let mut mut_state = state.get_states_mut(); @@ -428,55 +347,6 @@ where populations.push(mutations); } } -#[cfg(test)] -mod de_binomial_crossover { - use crate::framework::Individual; - use crate::problems::bmf::BenchmarkFunction; - use crate::state::common::Populations; - use crate::state::Random; - - use super::*; - - #[test] - fn all_recombined() { - let problem = BenchmarkFunction::sphere(3); - let comp = DEBinomialCrossover { pc: 1.0 }; - let mut state = State::new(); - state.insert(Random::testing()); - - let mut stack = Populations::::new(); - stack.push( - vec![ - vec![8.0, 4.0, 7.0, 3.0, 6.0, 2.0, 5.0, 1.0, 9.0, 0.0], - vec![0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], - ] - .into_iter() - .map(Individual::new_unevaluated) - .collect(), - ); - stack.push( - vec![ - vec![0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], - vec![8.0, 4.0, 7.0, 3.0, 6.0, 2.0, 5.0, 1.0, 9.0, 0.0], - ] - .into_iter() - .map(Individual::new_unevaluated) - .collect(), - ); - - state.insert(stack); - - comp.initialize(&problem, &mut state); - comp.execute(&problem, &mut state); - - let stack = state.populations_mut(); - - let offspring = stack.pop(); - let parents = stack.current(); - - assert_eq!(offspring.len(), parents.len()); - } -} /// Performs an exponential crossover, combining two individuals from two populations at the same index. /// @@ -488,14 +358,14 @@ pub struct DEExponentialCrossover { pc: f64, } impl DEExponentialCrossover { - pub fn new> + VectorProblem>(pc: f64) -> Box> { + pub fn new>(pc: f64) -> Box> { Box::new(Self { pc }) } } impl

Component

for DEExponentialCrossover where - P: Problem> + VectorProblem, + P: Problem + VectorProblem, { fn execute(&self, problem: &P, state: &mut State

) { let mut mut_state = state.get_states_mut(); @@ -526,52 +396,3 @@ where populations.push(mutations); } } -#[cfg(test)] -mod de_exponential_crossover { - use crate::framework::Individual; - use crate::problems::bmf::BenchmarkFunction; - use crate::state::common::Populations; - use crate::state::Random; - - use super::*; - - #[test] - fn all_recombined() { - let problem = BenchmarkFunction::sphere(3); - let comp = DEExponentialCrossover { pc: 1.0 }; - let mut state = State::new(); - state.insert(Random::testing()); - - let mut stack = Populations::::new(); - stack.push( - vec![ - vec![8.0, 4.0, 7.0, 3.0, 6.0, 2.0, 5.0, 1.0, 9.0, 0.0], - vec![0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], - ] - .into_iter() - .map(Individual::new_unevaluated) - .collect(), - ); - stack.push( - vec![ - vec![0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], - vec![8.0, 4.0, 7.0, 3.0, 6.0, 2.0, 5.0, 1.0, 9.0, 0.0], - ] - .into_iter() - .map(Individual::new_unevaluated) - .collect(), - ); - - state.insert(stack); - - comp.initialize(&problem, &mut state); - comp.execute(&problem, &mut state); - - let stack = state.populations_mut(); - - let offspring = stack.pop(); - let parents = stack.current(); - - assert_eq!(offspring.len(), parents.len()); - } -} diff --git a/src/components/initialization.rs b/src/components/initialization.rs index 56805651..e7ed059f 100644 --- a/src/components/initialization.rs +++ b/src/components/initialization.rs @@ -64,7 +64,7 @@ impl RandomSpread { pub fn new_init(initial_population_size: u32) -> Box> where D: SampleUniform + Clone + PartialOrd + 'static, - P: Problem> + LimitedVectorProblem, + P: Problem> + LimitedVectorProblem, { Box::new(Initializer(Self { initial_population_size: Some(initial_population_size), @@ -79,13 +79,15 @@ impl RandomSpread { ) -> Vec where D: SampleUniform + Clone + PartialOrd + 'static, - P: Problem> + LimitedVectorProblem, + P: Problem> + LimitedVectorProblem, { let mut population = Vec::new(); for _ in 0..population_size { - let solution = (0..problem.dimension()) - .map(|d| rng.gen_range(problem.range(d))) + let solution = problem + .domain() + .into_iter() + .map(|range| rng.gen_range(range)) .collect::>(); population.push(solution); @@ -97,7 +99,7 @@ impl RandomSpread { impl Initialization

for RandomSpread where D: SampleUniform + Clone + PartialOrd + 'static, - P: Problem> + LimitedVectorProblem, + P: Problem> + LimitedVectorProblem, { fn initialize_population(&self, problem: &P, state: &mut State

) -> Vec> { let population_size = self.initial_population_size.unwrap(); @@ -119,7 +121,7 @@ impl RandomPermutation { /// Creates this component as an initializer, pushing a new population on the stack. pub fn new_init

(initial_population_size: u32) -> Box> where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { Box::new(Initializer(Self { initial_population_size: Some(initial_population_size), @@ -133,7 +135,7 @@ impl RandomPermutation { population_size: u32, ) -> Vec where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { let mut population = Vec::new(); for _ in 0..population_size { @@ -147,7 +149,7 @@ impl RandomPermutation { impl

Initialization

for RandomPermutation where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { fn initialize_population(&self, problem: &P, state: &mut State

) -> Vec> { let population_size = self.initial_population_size.unwrap(); @@ -173,7 +175,7 @@ impl RandomBitstring { /// Creates this component as an initializer, pushing a new population on the stack. pub fn new_init

(initial_population_size: u32, p: f64) -> Box> where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { Box::new(Initializer(Self { initial_population_size: Some(initial_population_size), @@ -186,7 +188,7 @@ impl RandomBitstring { /// Creates this component as an initializer, pushing a new population on the stack. pub fn new_uniform_init

(initial_population_size: u32) -> Box> where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { Box::new(Initializer(Self { initial_population_size: Some(initial_population_size), @@ -201,7 +203,7 @@ impl RandomBitstring { population_size: u32, ) -> Vec where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { let mut population = Vec::new(); for _ in 0..population_size { @@ -216,7 +218,7 @@ impl RandomBitstring { impl

Initialization

for RandomBitstring where - P: Problem> + VectorProblem, + P: Problem> + VectorProblem, { fn initialize_population(&self, problem: &P, state: &mut State

) -> Vec> { let population_size = self.initial_population_size.unwrap(); diff --git a/src/components/selection.rs b/src/components/selection.rs index fe8e459f..6bc3b13d 100644 --- a/src/components/selection.rs +++ b/src/components/selection.rs @@ -155,10 +155,7 @@ impl Selection

for DuplicateSingle { ) -> Vec<&'p Individual

> { assert_eq!(population.len(), 1); let single_solution = population.first().unwrap(); - (0..self.offspring) - .into_iter() - .map(|_| single_solution) - .collect() + (0..self.offspring).map(|_| single_solution).collect() } } diff --git a/src/conditions/termination.rs b/src/conditions/termination.rs index b6ee34ea..c1e93b96 100644 --- a/src/conditions/termination.rs +++ b/src/conditions/termination.rs @@ -3,7 +3,7 @@ use crate::conditions::Condition; use crate::{ framework::SingleObjective, - problems::{HasKnownOptimum, HasKnownTarget, Problem, SingleObjectiveProblem}, + problems::{KnownOptimumProblem, OptimumReachedProblem, Problem, SingleObjectiveProblem}, state::{ common::{Evaluations, Iterations, Progress}, CustomState, State, @@ -18,7 +18,7 @@ pub struct TargetHit; impl TargetHit { pub fn new

() -> Box> where - P: SingleObjectiveProblem + HasKnownTarget, + P: SingleObjectiveProblem + OptimumReachedProblem, { Box::new(Self) } @@ -26,11 +26,11 @@ impl TargetHit { impl

Condition

for TargetHit where - P: SingleObjectiveProblem + HasKnownTarget, + P: SingleObjectiveProblem + OptimumReachedProblem, { fn evaluate(&self, problem: &P, state: &mut State

) -> bool { if let Some(fitness) = state.best_objective_value() { - !problem.target_hit(*fitness) + !problem.optimum_reached(*fitness) } else { false } @@ -193,7 +193,7 @@ pub struct DistanceToOpt { } impl DistanceToOpt { - pub fn new(distance: f64) -> Box> + pub fn new(distance: f64) -> Box> where P: SingleObjectiveProblem, { @@ -201,7 +201,7 @@ impl DistanceToOpt { } } -impl Condition

for DistanceToOpt +impl Condition

for DistanceToOpt where P: Problem, { diff --git a/src/framework/individual.rs b/src/framework/individual.rs deleted file mode 100644 index aaf43f7c..00000000 --- a/src/framework/individual.rs +++ /dev/null @@ -1,124 +0,0 @@ -use std::fmt::{Debug, Formatter}; - -use crate::problems::Problem; - -/// An encoded solution with an associated (optional) objective value. -pub struct Individual { - solution: P::Encoding, - objective: Option, -} - -impl Individual

{ - /// Constructs a new `Individual` from a given solution and objective value. - pub fn new(solution: P::Encoding, objective: P::Objective) -> Self { - Self { - solution, - objective: Some(objective), - } - } - - // Constructs a new `Individual` from a solution, leaving it unevaluated. - pub fn new_unevaluated(solution: P::Encoding) -> Self { - Self { - solution, - objective: None, - } - } - - /// Evaluates the `Individual` with some objective value. - /// - /// Note that this method is usually called with an objective value generated by a [Problem]. - /// - /// # Panics - /// - /// Panics if the individual already contains a valid objective value. - pub fn evaluate(&mut self, objective: P::Objective) { - if self.objective.is_some() { - // TODO: this should only emit a warning or maybe just be ignored. - panic!("Individual got evaluated twice"); - } - self.objective = Some(objective); - } - - /// Returns the individuals solution. - pub fn solution(&self) -> &P::Encoding { - &self.solution - } - - /// Returns the mutable individuals solution, resetting the objective value. - pub fn solution_mut(&mut self) -> &mut P::Encoding { - self.objective = None; - &mut self.solution - } - - /// Returns the individuals solution, consuming the `Individual` in the process. - pub fn into_solution(self) -> P::Encoding { - self.solution - } - - /// Returns `true` if the `Individual` is evaluated. - pub fn is_evaluated(&self) -> bool { - self.objective.is_some() - } - - /// Returns the objective as `Option`. - pub fn optional_objective(&self) -> Option<&P::Objective> { - self.objective.as_ref() - } - - /// Returns the objective value. - /// - /// # Panics - /// - /// Panics if the individual is not evaluated. - /// - /// Use [Individual::is_evaluated] to check for evaluation beforehand, - /// or use [Individual::optional_objective] directly to avoid panicking. - pub fn objective(&self) -> &P::Objective { - self.objective.as_ref().unwrap() - } -} - -impl

Individual

-where - P: Problem, - P::Encoding: Default, -{ - /// Construct a pseudo individual. - /// - /// Should only be used for testing. - pub fn new_test_unit(objective: P::Objective) -> Self { - Self::new(P::Encoding::default(), objective) - } -} - -impl Clone for Individual

{ - fn clone(&self) -> Self { - Self { - solution: self.solution.clone(), - objective: self.objective.clone(), - } - } -} - -impl PartialEq for Individual

{ - fn eq(&self, other: &Self) -> bool { - self.solution == other.solution && self.objective == other.objective - } -} - -impl> Debug for Individual

{ - fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result { - let objective = if let Some(objective) = self.optional_objective() { - format!("{:?}", objective) - } else { - format!("{:?}", None::) - }; - - write!( - f, - "Individual(solution={:?}, objective={})", - self.solution, objective - ) - } -} diff --git a/src/framework/mod.rs b/src/framework/mod.rs index 68e0353a..13cec435 100644 --- a/src/framework/mod.rs +++ b/src/framework/mod.rs @@ -5,10 +5,10 @@ use trait_set::trait_set; mod configuration; pub use configuration::{Configuration, ConfigurationBuilder}; -mod objective; +pub use crate::problems::objective; pub use objective::{IllegalObjective, MultiObjective, Objective, SingleObjective}; -mod individual; +pub use crate::problems::individual; pub use individual::Individual; trait_set! { diff --git a/src/framework/objective.rs b/src/framework/objective.rs deleted file mode 100644 index 00ad9306..00000000 --- a/src/framework/objective.rs +++ /dev/null @@ -1,215 +0,0 @@ -//! Utility type to store an individuals fitness. - -use std::{any::Any, fmt}; - -use trait_set::trait_set; - -trait_set! { - /// Collection of traits required by every objective. - pub trait AnyObjective = fmt::Debug + Clone + Eq + Any + PartialOrd + Send + Sync -} - -/// Marker trait for objectives. -/// -/// See [SingleObjective] and [MultiObjective]. -pub trait Objective: AnyObjective {} - -/// Error type for illegal objective values. -/// -/// Currently, `NaN` and `-Inf` are considered illegal. -#[derive(Clone, Copy, Debug, PartialEq, Eq)] -pub enum IllegalObjective { - NaN, - NegativeInfinity, -} - -impl fmt::Display for IllegalObjective { - fn fmt(&self, f: &mut fmt::Formatter) -> Result<(), fmt::Error> { - write!( - f, - "illegal objective: {}", - match self { - IllegalObjective::NaN => "NaN", - IllegalObjective::NegativeInfinity => "-Inf", - }, - ) - } -} - -/// Represents a single real-valued objective. -/// -/// Can be used to represent a single objective in single-objective optimization, -/// or a combined objective in multi-objective optimization. -/// -/// This objective type is used for [SingleObjectiveProblem](crate::problems::SingleObjectiveProblem)'s. It defaults to [f64::INFINITY]. -/// -/// # Restrictions -/// -/// This is a wrapper around [f64], which can't take NaN values, and therefore can implement -/// [Eq] and [Ord]. For details, see [IllegalObjective]. -#[derive(Clone, Copy, serde::Serialize)] -pub struct SingleObjective(f64); - -impl PartialEq for SingleObjective { - fn eq(&self, other: &Self) -> bool { - self.0 == other.0 - } -} -impl Eq for SingleObjective {} - -impl PartialOrd for SingleObjective { - fn partial_cmp(&self, other: &Self) -> Option { - self.0.partial_cmp(&other.0) - } -} -impl Ord for SingleObjective { - fn cmp(&self, other: &Self) -> std::cmp::Ordering { - self.partial_cmp(other).unwrap() - } -} - -impl Default for SingleObjective { - fn default() -> Self { - Self(f64::INFINITY) - } -} - -impl Objective for SingleObjective {} - -impl SingleObjective { - /// Checks if the objective is positive infinity or not. - pub fn is_finite(&self) -> bool { - self.0.is_finite() - } - - /// Returns the objective value as float. - pub fn value(&self) -> f64 { - self.0 - } -} - -impl From for f64 { - fn from(objective: SingleObjective) -> Self { - objective.value() - } -} - -impl TryFrom for SingleObjective { - type Error = IllegalObjective; - - /// Tries to convert a float into a `SingleObjective` value. - /// - /// See [IllegalObjective] for a list of illegal values. - /// All other values will return `Ok`. - fn try_from(value: f64) -> Result { - match value { - _ if value.is_nan() => Err(IllegalObjective::NaN), - _ if value.is_infinite() && value.is_sign_negative() => { - Err(IllegalObjective::NegativeInfinity) - } - _ => Ok(SingleObjective(value)), - } - } -} - -impl fmt::Debug for SingleObjective { - fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { - write!(f, "{:?}", self.0) - } -} - -/// Represents multiple real-valued objectives. -/// -/// Can be used to represent an objective vector in multi-objective optimization. -/// -/// This objective type is used for [MultiObjectiveProblem](crate::problems::MultiObjectiveProblem)'s. -/// -/// # Restrictions -/// -/// This is a wrapper around a [Vec] of [f64], which can't take NaN values, and therefore can implement -/// [Eq]. For details, see [IllegalObjective]. -/// -/// The [PartialOrd] implementation uses pareto-domination to decide on the order. -/// Note that pareto-domination may not always yield an ordering, so [Ord] is not implemented. -#[derive(Clone, serde::Serialize)] -pub struct MultiObjective(Vec); - -impl PartialEq for MultiObjective { - fn eq(&self, other: &Self) -> bool { - self.0 == other.0 - } -} -impl Eq for MultiObjective {} - -/// Implements Pareto-Domination. -impl PartialOrd for MultiObjective { - fn partial_cmp(&self, other: &Self) -> Option { - // Use Eq checking for equality - if self == other { - return Some(std::cmp::Ordering::Equal); - } - - let dominates = self - .value() - .iter() - .zip(other.value().iter()) - .filter(|(own, other)| own >= other) - .count(); - - match dominates { - // Self dominates - x if x == self.value().len() => Some(std::cmp::Ordering::Greater), - // Other dominates - 0 => Some(std::cmp::Ordering::Less), - // None dominates - _ => None, - } - } -} - -impl fmt::Debug for MultiObjective { - fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result { - write!(f, "{:?}", self.0) - } -} - -impl Objective for MultiObjective {} - -impl MultiObjective { - /// Checks if the objective vector contains positive infinity or not. - pub fn is_finite(&self) -> bool { - self.0.iter().all(|o| o.is_finite()) - } - - /// Returns the objective value as a vector of floats. - pub fn value(&self) -> &[f64] { - &self.0 - } -} - -impl From for Vec { - fn from(objective: MultiObjective) -> Self { - objective.0 - } -} - -impl TryFrom> for MultiObjective { - type Error = IllegalObjective; - - /// Tries to convert a vector of floats into a `MultiObjective` value. - /// - /// See [IllegalObjective] for a list of illegal values. - /// All other values will return `Ok`. - fn try_from(value: Vec) -> Result { - match value { - _ if value.iter().any(|o| o.is_nan()) => Err(IllegalObjective::NaN), - _ if value - .iter() - .any(|o| o.is_infinite() && o.is_sign_negative()) => - { - Err(IllegalObjective::NegativeInfinity) - } - _ => Ok(MultiObjective(value)), - } - } -} diff --git a/src/heuristics/aco.rs b/src/heuristics/aco.rs index e5a4652e..9fd61a4b 100644 --- a/src/heuristics/aco.rs +++ b/src/heuristics/aco.rs @@ -4,7 +4,7 @@ use crate::{ components::*, conditions::Condition, framework::Configuration, - problems::{tsp, SingleObjectiveProblem}, + problems::{SingleObjectiveProblem, TravellingSalespersonProblem}, }; /// Parameters for [ant_system]. @@ -22,11 +22,11 @@ pub struct ASParameters { /// /// # References /// [doi.org/10.1109/MCI.2006.329691](https://doi.org/10.1109/MCI.2006.329691) -pub fn ant_system( +pub fn ant_system( params: ASParameters, - termination: Box>, - logger: Box>, -) -> Configuration { + termination: Box>, + logger: Box>, +) -> Configuration

{ let ASParameters { number_of_ants, alpha, @@ -70,11 +70,11 @@ pub struct MMASParameters { /// /// # References /// [doi.org/10.1109/MCI.2006.329691](https://doi.org/10.1109/MCI.2006.329691) -pub fn max_min_ant_system( +pub fn max_min_ant_system( params: MMASParameters, - termination: Box>, - logger: Box>, -) -> Configuration { + termination: Box>, + logger: Box>, +) -> Configuration

{ let MMASParameters { number_of_ants, alpha, @@ -142,7 +142,7 @@ pub mod ant_ops { use crate::{ components::*, framework::{Individual, SingleObjective}, - problems::tsp::SymmetricTsp, + problems::TravellingSalespersonProblem, state::{PheromoneMatrix, Random, State}, }; @@ -154,12 +154,12 @@ pub mod ant_ops { pub default_pheromones: f64, } impl AcoGeneration { - pub fn new( + pub fn new( number_of_ants: usize, alpha: f64, beta: f64, default_pheromones: f64, - ) -> Box> { + ) -> Box> { Box::new(Self { number_of_ants, alpha, @@ -168,22 +168,22 @@ pub mod ant_ops { }) } } - impl Component for AcoGeneration { - fn initialize(&self, problem: &SymmetricTsp, state: &mut State) { + impl Component

for AcoGeneration { + fn initialize(&self, problem: &P, state: &mut State

) { state.insert(PheromoneMatrix::new( - problem.dimension, + problem.dimension(), self.default_pheromones, )); } - fn execute(&self, problem: &SymmetricTsp, state: &mut State) { + fn execute(&self, problem: &P, state: &mut State

) { let (pm, rng) = state.get_multiple_mut::<(PheromoneMatrix, Random)>(); let mut routes = Vec::new(); // Greedy route { - let mut remaining = (1..problem.dimension).into_iter().collect::>(); - let mut route = Vec::with_capacity(problem.dimension); + let mut remaining = (1..problem.dimension()).collect::>(); + let mut route = Vec::with_capacity(problem.dimension()); route.push(0); while !remaining.is_empty() { let last = *route.last().unwrap(); @@ -201,8 +201,8 @@ pub mod ant_ops { // Probabilistic routes for _ in 0..self.number_of_ants { - let mut remaining = (1..problem.dimension).into_iter().collect::>(); - let mut route = Vec::with_capacity(problem.dimension); + let mut remaining = (1..problem.dimension()).collect::>(); + let mut route = Vec::with_capacity(problem.dimension()); route.push(0); while !remaining.is_empty() { let last = *route.last().unwrap(); @@ -222,7 +222,7 @@ pub mod ant_ops { let population = routes .into_iter() - .map(Individual::::new_unevaluated) + .map(Individual::

::new_unevaluated) .collect(); *state.populations_mut().current_mut() = population; } @@ -234,19 +234,22 @@ pub mod ant_ops { pub decay_coefficient: f64, } impl AsPheromoneUpdate { - pub fn new(evaporation: f64, decay_coefficient: f64) -> Box> { + pub fn new( + evaporation: f64, + decay_coefficient: f64, + ) -> Box> { Box::new(Self { evaporation, decay_coefficient, }) } } - impl Component for AsPheromoneUpdate { - fn initialize(&self, _problem: &SymmetricTsp, state: &mut State) { + impl Component

for AsPheromoneUpdate { + fn initialize(&self, _problem: &P, state: &mut State

) { state.require::(); } - fn execute(&self, _problem: &SymmetricTsp, state: &mut State) { + fn execute(&self, _problem: &P, state: &mut State

) { let mut mut_state = state.get_states_mut(); let pm = mut_state.get_mut::(); let population = mut_state.populations().current(); @@ -274,11 +277,11 @@ pub mod ant_ops { pub min_pheromones: f64, } impl MinMaxPheromoneUpdate { - pub fn new( + pub fn new( evaporation: f64, max_pheromones: f64, min_pheromones: f64, - ) -> Box> { + ) -> Box> { assert!( min_pheromones < max_pheromones, "min_pheromones must be less than max_pheromones" @@ -290,12 +293,12 @@ pub mod ant_ops { }) } } - impl Component for MinMaxPheromoneUpdate { - fn initialize(&self, _problem: &SymmetricTsp, state: &mut State) { + impl Component

for MinMaxPheromoneUpdate { + fn initialize(&self, _problem: &P, state: &mut State

) { state.require::(); } - fn execute(&self, _problem: &SymmetricTsp, state: &mut State) { + fn execute(&self, _problem: &P, state: &mut State

) { let mut mut_state = state.get_states_mut(); let pm = mut_state.get_mut::(); let population = mut_state.populations().current(); diff --git a/src/heuristics/cro.rs b/src/heuristics/cro.rs index fcb4d781..51c7f723 100644 --- a/src/heuristics/cro.rs +++ b/src/heuristics/cro.rs @@ -29,7 +29,9 @@ pub fn real_cro

( logger: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem + LimitedVectorProblem, + P: SingleObjectiveProblem> + + VectorProblem + + LimitedVectorProblem, { let RealProblemParameters { initial_population_size, diff --git a/src/heuristics/de.rs b/src/heuristics/de.rs index e359244f..c21ae1dd 100644 --- a/src/heuristics/de.rs +++ b/src/heuristics/de.rs @@ -26,7 +26,9 @@ pub fn real_de

( logger: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem + LimitedVectorProblem, + P: SingleObjectiveProblem> + + VectorProblem + + LimitedVectorProblem, { let RealProblemParameters { population_size, diff --git a/src/heuristics/es.rs b/src/heuristics/es.rs index 80bfe40e..dbec34d3 100644 --- a/src/heuristics/es.rs +++ b/src/heuristics/es.rs @@ -25,7 +25,9 @@ pub fn real_mu_plus_lambda

( logger: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem + LimitedVectorProblem, + P: SingleObjectiveProblem> + + VectorProblem + + LimitedVectorProblem, { let RealProblemParameters { population_size, diff --git a/src/heuristics/ga.rs b/src/heuristics/ga.rs index 4077b33f..c858725f 100644 --- a/src/heuristics/ga.rs +++ b/src/heuristics/ga.rs @@ -26,7 +26,7 @@ pub fn binary_ga

( ) -> Configuration

where P: SingleObjectiveProblem> - + VectorProblem + + VectorProblem + LimitedVectorProblem, { let BinaryProblemParameters { @@ -77,7 +77,9 @@ pub fn real_ga

( logger: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem + LimitedVectorProblem, + P: SingleObjectiveProblem> + + VectorProblem + + LimitedVectorProblem, { let RealProblemParameters { population_size, diff --git a/src/heuristics/ils.rs b/src/heuristics/ils.rs index f0463c9f..01f18322 100644 --- a/src/heuristics/ils.rs +++ b/src/heuristics/ils.rs @@ -22,7 +22,9 @@ pub fn real_iterated_local_search

( termination: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem + LimitedVectorProblem, + P: SingleObjectiveProblem> + + VectorProblem + + LimitedVectorProblem, { let RealProblemParameters { local_search_params, @@ -57,7 +59,7 @@ pub fn permutation_iterated_local_search

( termination: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem, + P: SingleObjectiveProblem> + VectorProblem, { let PermutationProblemParameters { local_search_params, diff --git a/src/heuristics/iwo.rs b/src/heuristics/iwo.rs index 27bb5692..21a7de02 100644 --- a/src/heuristics/iwo.rs +++ b/src/heuristics/iwo.rs @@ -34,7 +34,9 @@ pub fn real_iwo

( termination: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem + LimitedVectorProblem, + P: SingleObjectiveProblem> + + VectorProblem + + LimitedVectorProblem, { let RealProblemParameters { initial_population_size, diff --git a/src/heuristics/ls.rs b/src/heuristics/ls.rs index 0b0b6816..ae9a5733 100644 --- a/src/heuristics/ls.rs +++ b/src/heuristics/ls.rs @@ -21,7 +21,9 @@ pub fn real_local_search

( termination: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem + LimitedVectorProblem, + P: SingleObjectiveProblem> + + VectorProblem + + LimitedVectorProblem, { let RealProblemParameters { n_neighbors, @@ -57,7 +59,7 @@ pub fn permutation_local_search

( termination: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem, + P: SingleObjectiveProblem> + VectorProblem, { let PermutationProblemParameters { n_neighbors, diff --git a/src/heuristics/pso.rs b/src/heuristics/pso.rs index 542eab54..f68b8897 100644 --- a/src/heuristics/pso.rs +++ b/src/heuristics/pso.rs @@ -25,7 +25,7 @@ pub fn real_pso

( termination: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + LimitedVectorProblem + 'static, + P: SingleObjectiveProblem> + LimitedVectorProblem + 'static, { let RealProblemParameters { num_particles, diff --git a/src/heuristics/rs.rs b/src/heuristics/rs.rs index a0782928..986f5971 100644 --- a/src/heuristics/rs.rs +++ b/src/heuristics/rs.rs @@ -14,7 +14,9 @@ pub fn real_random_search

( logger: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem + LimitedVectorProblem, + P: SingleObjectiveProblem> + + VectorProblem + + LimitedVectorProblem, { Configuration::builder() .do_(generation::RandomSpread::new_init(1)) @@ -37,7 +39,7 @@ pub fn permutation_random_search

( logger: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem, + P: SingleObjectiveProblem> + VectorProblem, { Configuration::builder() .do_(generation::RandomPermutation::new_init(1)) diff --git a/src/heuristics/rw.rs b/src/heuristics/rw.rs index e912726d..34b11775 100644 --- a/src/heuristics/rw.rs +++ b/src/heuristics/rw.rs @@ -20,7 +20,7 @@ pub fn real_random_walk

( logger: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + LimitedVectorProblem, + P: SingleObjectiveProblem> + LimitedVectorProblem, { let RealProblemParameters { deviation } = params; @@ -50,7 +50,7 @@ pub fn permutation_random_walk

( logger: Box>, ) -> Configuration

where - P: SingleObjectiveProblem> + VectorProblem, + P: SingleObjectiveProblem> + VectorProblem, { let PermutationProblemParameters { n_swap } = params; diff --git a/src/lib.rs b/src/lib.rs index b72e16c9..48489e85 100644 --- a/src/lib.rs +++ b/src/lib.rs @@ -1,32 +1,31 @@ -#![allow( - clippy::too_many_arguments, - clippy::type_complexity, - clippy::new_ret_no_self -)] -#![doc = include_str!("../docs/overview.md")] +//! A framework for the modular construction and evaluation of metaheuristics. -pub mod components; -pub use components::Component; +#![allow(clippy::new_ret_no_self)] +pub mod components; pub mod conditions; -pub use conditions::Condition; - pub mod framework; -pub use framework::{Configuration, Individual}; - pub mod heuristics; +pub mod population; pub mod prelude; pub mod problems; pub mod state; -pub use state::{CustomState, Random, State}; - +pub(crate) mod testing; pub mod tracking; pub mod utils; -#[cfg(test)] -pub mod testing; +// Re-exports from modules +pub use components::Component; +pub use conditions::Condition; +pub use framework::Configuration; +pub use problems::{ + individual::Individual, + objective::{MultiObjective, Objective, SingleObjective}, + MultiObjectiveProblem, Problem, SingleObjectiveProblem, +}; +pub use state::{CustomState, Random, State}; -// re-exports +// Crate re-exports pub use derive_more; pub use float_eq; pub use rand; diff --git a/src/population.rs b/src/population.rs new file mode 100644 index 00000000..5a1f1fde --- /dev/null +++ b/src/population.rs @@ -0,0 +1,262 @@ +//! Helper traits for dealing with collections of individuals, i.e. populations, and obtaining +//! `&`, `&mut` or owned solutions from them. +//! +//! Most traits have blanket implementations for types that implement `IntoIterator`, +//! which means that manual implementation is not necessary. + +use std::ops::{Deref, DerefMut}; + +use crate::{problems::SingleObjectiveProblem, Individual, Problem}; + +/// Trait for obtaining solution references from a collection of [`Individual`]s. +/// +/// Internally, [`Individual::solution`] is called on every individual. +/// +/// The trait is automatically implemented for types which implement [`IntoIterator(population: &[Individual

]) { +/// for solution in population.as_solutions() { +/// // Do something with each solution (`&P::Encoding`). +/// } +/// } +/// ``` +pub trait AsSolutions<'a, P: Problem> { + /// Obtains solution references from a collection of [`Individual`]s. + fn as_solutions(&'a self) -> Vec<&'a P::Encoding>; +} + +impl<'a, P, T> AsSolutions<'a, P> for T +where + P: Problem, + T: Deref + ?Sized + 'a, + &'a T::Target: IntoIterator>, +{ + fn as_solutions(&'a self) -> Vec<&'a P::Encoding> { + self.deref().into_iter().map(Individual::solution).collect() + } +} + +/// Trait for obtaining mutable solution references from a collection of [`Individual`]s. +/// +/// Internally, [`Individual::solution_mut`] is called on every individual, which means that +/// all objective values are reset. +/// +/// The trait is automatically implemented for types which implement [`IntoIterator(mut population: &mut [Individual

]) { +/// for solution in population.as_solutions_mut() { +/// // Do something with each mutable solution (`&mut P::Encoding`). +/// } +/// } +/// ``` +pub trait AsSolutionsMut<'a, P: Problem> { + /// Obtains mutable solution references from a collection of [`Individual`]s. + fn as_solutions_mut(&'a mut self) -> Vec<&'a mut P::Encoding>; +} + +impl<'a, P, T> AsSolutionsMut<'a, P> for T +where + P: Problem, + T: DerefMut + ?Sized + 'a, + &'a mut T::Target: IntoIterator>, +{ + fn as_solutions_mut(&'a mut self) -> Vec<&'a mut P::Encoding> { + self.deref_mut() + .into_iter() + .map(Individual::solution_mut) + .collect() + } +} + +/// Trait for converting a collection of [`Individual`]s into their solutions. +/// +/// Internally, [`Individual::into_solution`] is called on every individual. +/// +/// The trait is automatically implemented for types which implement [`IntoIterator(population: Vec>) { +/// for solution in population.into_solutions() { +/// // Do something with each owned solution (`P::Encoding`). +/// } +/// } +/// ``` +pub trait IntoSolutions { + /// Converts a collection of [`Individual`]s into their solutions. + fn into_solutions(self) -> Vec + where + Self: Sized; +} + +impl IntoSolutions

for T +where + P: Problem, + T: IntoIterator>, +{ + fn into_solutions(self) -> Vec + where + Self: Sized, + { + self.into_iter().map(Individual::into_solution).collect() + } +} + +/// Trait for converting solutions into a collection of [`Individual`]s . +/// +/// Internally, [`Individual::new_unevaluated`] is called on every solution. +/// +/// The trait is automatically implemented for types which implement [`IntoIterator(solutions: Vec) { +/// for individual in solutions.into_individuals::

() { +/// // Do something with each individual. +/// } +/// } +/// ``` +pub trait IntoIndividuals { + /// Converts solutions into a collection of [`Individual`]s. + fn into_individuals>(self) -> Vec> + where + Self: Sized; +} + +impl IntoIndividuals for T +where + T: IntoIterator, +{ + fn into_individuals>(self) -> Vec> + where + Self: Sized, + { + self.into_iter().map(Individual::new_unevaluated).collect() + } +} + +/// Trait for converting a collection of [`Individual`]s into its single [`Individual`]. +/// +/// For unwrapping a collection of `&`[`Individual`]s into its single `&`[`Individual`], see [`IntoSingleRef`]. +/// +/// Note that this functionality cannot be merged with [`IntoSingleRef`], as the trait bounds necessary +/// for two blanket implementation for `Individual` and `&Individual` are not expressible yet. +/// +/// # Examples +/// +/// ``` +/// use mahf::{Individual, Problem}; +/// use mahf::population::IntoSingle; +/// +/// pub fn example(population: Vec>) { +/// let single: Option> = population.into_single(); +/// } +/// ``` +pub trait IntoSingle { + /// Converts a collection of [`Individual`]s into its single [`Individual`]. + fn into_single(self) -> Option>; +} + +impl IntoSingle

for T +where + P: Problem, + T: IntoIterator>, +{ + fn into_single(self) -> Option> { + self.into_iter().next() + } +} + +/// Trait for converting a collection of `&`[`Individual`]s into its single `&`[`Individual`]. +/// +/// For unwrapping a collection of [`Individual`]s into its single [`Individual`], see [`IntoSingle`]. +/// +/// Note that this functionality cannot be merged with [`IntoSingle`], as the trait bounds necessary +/// for two blanket implementation for `Individual` and `&'a Individual` are not expressible yet. +/// +/// # Examples +/// +/// ``` +/// use mahf::{Individual, Problem}; +/// use mahf::population::IntoSingleRef; +/// +/// pub fn example(population: &[Individual

]) { +/// let single: Option<&Individual

> = population.into_single_ref(); +/// } +/// ``` +pub trait IntoSingleRef<'a, P: Problem> { + /// Converts a collection of `&`[`Individual`]s into its single `&`[`Individual`]. + fn into_single_ref(self) -> Option<&'a Individual

>; +} + +impl<'a, P, T> IntoSingleRef<'a, P> for T +where + P: Problem, + T: IntoIterator> + 'a, +{ + fn into_single_ref(self) -> Option<&'a Individual

> { + self.into_iter().next() + } +} + +/// Trait for obtaining a reference to the best [`Individual`] from a collection of [`Individual`]s, +/// i.e. the individual with lowest objective value. +/// +/// # Examples +/// +/// ``` +/// use mahf::{Individual, SingleObjectiveProblem}; +/// use mahf::population::BestIndividual; +/// +/// pub fn example(population: &[Individual

]) { +/// let best: Option<&Individual

> = population.best_individual(); +/// +/// for individual in population { +/// // `best` has the lowest objective value. +/// assert!(best.unwrap().objective() <= individual.objective()); +/// } +/// } +/// ``` +pub trait BestIndividual<'a, P: SingleObjectiveProblem> { + /// Obtains a reference to the best [`Individual`] from a collection of [`Individual`]s. + fn best_individual(&'a self) -> Option<&'a Individual

>; +} + +impl<'a, P, T> BestIndividual<'a, P> for T +where + P: SingleObjectiveProblem, + T: Deref + ?Sized + 'a, + &'a T::Target: IntoIterator>, +{ + fn best_individual(&'a self) -> Option<&'a Individual

> { + self.deref().into_iter().min_by_key(|i| i.objective()) + } +} diff --git a/src/problems/bmf/implementations.rs b/src/problems/bmf/implementations.rs deleted file mode 100644 index 0e4d8803..00000000 --- a/src/problems/bmf/implementations.rs +++ /dev/null @@ -1,1679 +0,0 @@ -//! Collection of test functions from [benchmarkfcns.xyz](http://benchmarkfcns.xyz) without Quartic function and Xin-She Yang Nr. 1 function - -use crate::problems::bmf::BenchmarkFunction; -use std::f64::consts::PI; - -impl BenchmarkFunction { - pub fn name(&self) -> &'static str { - self.name - } - - pub fn dimension(&self) -> usize { - self.dimension - } - - pub fn domain(&self) -> [f64; 2] { - self.domain - } - - pub fn known_optimum(&self) -> f64 { - self.known_optimum - } -} - -impl BenchmarkFunction { - /// The [Sphere](http://benchmarkfcns.xyz/benchmarkfcns/spherefcn.html) function. - pub fn sphere(dimension: usize) -> Self { - BenchmarkFunction { - name: "sphere", - implementation: scaled_implementations::sphere, - dimension, - domain: [-5.12, 5.12], - known_optimum: 0.0, - } - } - - /// The [Rastrigin](http://benchmarkfcns.xyz/benchmarkfcns/rastriginfcn.html) function. - pub fn rastrigin(dimension: usize) -> Self { - BenchmarkFunction { - name: "rastrigin", - implementation: scaled_implementations::rastrigin, - dimension, - domain: [-5.12, 5.12], - known_optimum: 0.0, - } - } - - /// The [Ackley](http://benchmarkfcns.xyz/benchmarkfcns/ackleyfcn.html) function. - pub fn ackley(dimension: usize) -> Self { - BenchmarkFunction { - name: "ackley", - implementation: scaled_implementations::ackley, - dimension, - domain: [-32.0, 32.0], - known_optimum: 0.0, - } - } - - /// The [AckleyN4](http://benchmarkfcns.xyz/benchmarkfcns/ackleyn4fcn.html) function. - pub fn ackley_n4(dimension: usize) -> Self { - BenchmarkFunction { - name: "ackleyN4", - implementation: scaled_implementations::ackley_n4, - dimension, - domain: [-35.0, 35.0], - known_optimum: -4.590101633799122, - } - } - - /// The [AlpineN1](http://benchmarkfcns.xyz/benchmarkfcns/alpinen1fcn.html) function. - pub fn alpine_n1(dimension: usize) -> Self { - BenchmarkFunction { - name: "alpineN1", - implementation: scaled_implementations::alpine_n1, - dimension, - domain: [-10.0, 10.0], - known_optimum: 0.0, - } - } - - /// The [AlpineN2](http://benchmarkfcns.xyz/benchmarkfcns/alpinen2fcn.html) function. - pub fn alpine_n2(dimension: usize) -> Self { - BenchmarkFunction { - name: "alpineN2", - implementation: scaled_implementations::alpine_n2, - dimension, - domain: [0.0, 10.0], - known_optimum: -(2.808_f64).powi(dimension as i32), - } - } - - /// The [Brown](http://benchmarkfcns.xyz/benchmarkfcns/brownfcn.html) function. - pub fn brown(dimension: usize) -> Self { - BenchmarkFunction { - name: "brown", - implementation: scaled_implementations::brown, - dimension, - domain: [-1.0, 4.0], - known_optimum: 0.0, - } - } - - /// The [Exponential](http://benchmarkfcns.xyz/benchmarkfcns/exponentialfcn.html) function. - pub fn exponential(dimension: usize) -> Self { - BenchmarkFunction { - name: "exponential", - implementation: scaled_implementations::exponential, - dimension, - domain: [-1.0, 1.0], - known_optimum: -1.0, - } - } - - /// The [Griewank](http://benchmarkfcns.xyz/benchmarkfcns/griewankfcn.html) function. - pub fn griewank(dimension: usize) -> Self { - BenchmarkFunction { - name: "griewank", - implementation: scaled_implementations::griewank, - dimension, - domain: [-600.0, 600.0], - known_optimum: 0.0, - } - } - - /// The [Happy Cat](http://benchmarkfcns.xyz/benchmarkfcns/happycatfcn.html) function. - pub fn happy_cat(dimension: usize) -> Self { - BenchmarkFunction { - name: "happyCat", - implementation: scaled_implementations::happy_cat, - dimension, - domain: [-2.0, 2.0], - known_optimum: 0.0, - } - } - - /// The [Periodic](http://benchmarkfcns.xyz/benchmarkfcns/periodicfcn.html) function. - pub fn periodic(dimension: usize) -> Self { - BenchmarkFunction { - name: "periodic", - implementation: scaled_implementations::periodic, - dimension, - domain: [-10.0, 10.0], - known_optimum: 0.9, - } - } - - /// The [Powell Sum](http://benchmarkfcns.xyz/benchmarkfcns/powellsumfcn.html) function. - pub fn powell_sum(dimension: usize) -> Self { - BenchmarkFunction { - name: "powellSum", - implementation: scaled_implementations::powell_sum, - dimension, - domain: [-1.0, 1.0], - known_optimum: 0.0, - } - } - - /// The [Qing](http://benchmarkfcns.xyz/benchmarkfcns/qingfcn.html) function. - pub fn qing(dimension: usize) -> Self { - BenchmarkFunction { - name: "qing", - implementation: scaled_implementations::qing, - dimension, - domain: [-500.0, 500.0], - known_optimum: 0.0, - } - } - - /// The [Ridge](http://benchmarkfcns.xyz/benchmarkfcns/ridgefcn.html) function. - pub fn ridge(dimension: usize) -> Self { - BenchmarkFunction { - name: "ridge", - implementation: scaled_implementations::ridge, - dimension, - domain: [-5.0, 5.0], - known_optimum: -5.0, - } - } - - /// The [Rosenbrock](http://benchmarkfcns.xyz/benchmarkfcns/rosenbrockfcn.html) function. - pub fn rosenbrock(dimension: usize) -> Self { - BenchmarkFunction { - name: "rosenbrock", - implementation: scaled_implementations::rosenbrock, - dimension, - domain: [-5.0, 10.0], - known_optimum: 0.0, - } - } - - /// The [Salomon](http://benchmarkfcns.xyz/benchmarkfcns/salomonfcn.html) function. - pub fn salomon(dimension: usize) -> Self { - BenchmarkFunction { - name: "salomon", - implementation: scaled_implementations::salomon, - dimension, - domain: [-100.0, 100.0], - known_optimum: 0.0, - } - } - - /// The [Schwefel 2.20](http://benchmarkfcns.xyz/benchmarkfcns/schwefel220fcn.html) function. - pub fn schwefel_220(dimension: usize) -> Self { - BenchmarkFunction { - name: "schwefel220", - implementation: scaled_implementations::schwefel_220, - dimension, - domain: [-100.0, 100.0], - known_optimum: 0.0, - } - } - - /// The [Schwefel 2.21](http://benchmarkfcns.xyz/benchmarkfcns/schwefel221fcn.html) function. - pub fn schwefel_221(dimension: usize) -> Self { - BenchmarkFunction { - name: "schwefel221", - implementation: scaled_implementations::schwefel_221, - dimension, - domain: [-100.0, 100.0], - known_optimum: 0.0, - } - } - - /// The [Schwefel 2.22](http://benchmarkfcns.xyz/benchmarkfcns/schwefel222fcn.html) function. - pub fn schwefel_222(dimension: usize) -> Self { - BenchmarkFunction { - name: "schwefel222", - implementation: scaled_implementations::schwefel_222, - dimension, - domain: [-100.0, 100.0], - known_optimum: 0.0, - } - } - - /// The [Schwefel 2.23](http://benchmarkfcns.xyz/benchmarkfcns/schwefel223fcn.html) function. - pub fn schwefel_223(dimension: usize) -> Self { - BenchmarkFunction { - name: "schwefel223", - implementation: scaled_implementations::schwefel_223, - dimension, - domain: [-10.0, 10.0], - known_optimum: 0.0, - } - } - - /// The [Schwefel](http://benchmarkfcns.xyz/benchmarkfcns/schwefelfcn.html) function. - pub fn schwefel(dimension: usize) -> Self { - BenchmarkFunction { - name: "schwefel", - implementation: scaled_implementations::schwefel, - dimension, - domain: [-500.0, 500.0], - known_optimum: 0.0, - } - } - - /// The [Shubert Nr. 3](http://benchmarkfcns.xyz/benchmarkfcns/shubert3fcn.html) function. - pub fn shubert_n3(dimension: usize) -> Self { - BenchmarkFunction { - name: "shubertN3", - implementation: scaled_implementations::shubert_n3, - dimension, - domain: [-10.0, 10.0], - known_optimum: -24.062499, - } - } - - /// The [Shubert Nr. 4](http://benchmarkfcns.xyz/benchmarkfcns/shubert4fcn.html) function. - pub fn shubert_n4(dimension: usize) -> Self { - BenchmarkFunction { - name: "shubertN4", - implementation: scaled_implementations::shubert_n4, - dimension, - domain: [-10.0, 10.0], - known_optimum: -25.740858, - } - } - - /// The [Shubert](http://benchmarkfcns.xyz/benchmarkfcns/shubertfcn.html) function. - pub fn shubert(dimension: usize) -> Self { - BenchmarkFunction { - name: "shubert", - implementation: scaled_implementations::shubert, - dimension, - domain: [-10.0, 10.0], - known_optimum: -186.7309, - } - } - - /// The [Styblinski-Tang](http://benchmarkfcns.xyz/benchmarkfcns/styblinskitankfcn.html) function. - pub fn styblinski_tang(dimension: usize) -> Self { - BenchmarkFunction { - name: "styblinskiTang", - implementation: scaled_implementations::styblinski_tang, - dimension, - domain: [-5.0, 5.0], - known_optimum: -39.1662 * dimension as f64, - } - } - - /// The [Sum Squares](http://benchmarkfcns.xyz/benchmarkfcns/sumsquaresfcn.html) function. - pub fn sum_squares(dimension: usize) -> Self { - BenchmarkFunction { - name: "sumSquares", - implementation: scaled_implementations::sum_squares, - dimension, - domain: [-10.0, 10.0], - known_optimum: 0.0, - } - } - - /// The [Xin-She Yang Nr. 2](http://benchmarkfcns.xyz/benchmarkfcns/xinsheyangn2fcn.html) function. - pub fn yang_n2(dimension: usize) -> Self { - BenchmarkFunction { - name: "yangN2", - implementation: scaled_implementations::yang_n2, - dimension, - domain: [-2.0 * PI, 2.0 * PI], - known_optimum: 0.0, - } - } - - /// The [Xin-She Yang Nr. 3](http://benchmarkfcns.xyz/benchmarkfcns/xinsheyangn3fcn.html) function. - pub fn yang_n3(dimension: usize) -> Self { - BenchmarkFunction { - name: "yangN3", - implementation: scaled_implementations::yang_n3, - dimension, - domain: [-2.0 * PI, 2.0 * PI], - known_optimum: -1.0, - } - } - - /// The [Xin-She Yang Nr. 4](http://benchmarkfcns.xyz/benchmarkfcns/xinsheyangn4fcn.html) function. - pub fn yang_n4(dimension: usize) -> Self { - BenchmarkFunction { - name: "yangN4", - implementation: scaled_implementations::yang_n4, - dimension, - domain: [-10.0, 10.0], - known_optimum: -1.0, - } - } - - /// The [Zakharov](http://benchmarkfcns.xyz/benchmarkfcns/zakharov.html) function. - pub fn zakharov(dimension: usize) -> Self { - BenchmarkFunction { - name: "zakharov", - implementation: scaled_implementations::zakharov, - dimension, - domain: [-10.0, 10.0], - known_optimum: 0.0, - } - } - - /// The [Ackley Nr. 2](http://benchmarkfcns.xyz/benchmarkfcns/ackleyn2fcn.html) function. - pub fn ackley_n2(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "ackleyN2", - implementation: scaled_implementations::ackley_n2, - dimension, - domain: [-32.0, 32.0], - known_optimum: -200.0, - } - } - - /// The [Ackley Nr. 3](http://benchmarkfcns.xyz/benchmarkfcns/ackleyn3fcn.html) function. - pub fn ackley_n3(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "ackleyN3", - implementation: scaled_implementations::ackley_n3, - dimension, - domain: [-32.0, 32.0], - known_optimum: -219.1418, - } - } - - /// The [Adjiman](http://benchmarkfcns.xyz/benchmarkfcns/adjimanfcn.html) function. - pub fn adjiman(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "adjiman", - implementation: scaled_implementations::adjiman, - dimension, - //TODO: find better solution when domains different for x and y - domain: [-2.0, 2.0], - known_optimum: -2.02181, - } - } - - /// The [Bartels Conn](http://benchmarkfcns.xyz/benchmarkfcns/bartelsconnfcn.html) function. - pub fn bartels_conn(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "bartelsConn", - implementation: scaled_implementations::bartels_conn, - dimension, - domain: [-500.0, 500.0], - known_optimum: 1.0, - } - } - - /// The [Beale](http://benchmarkfcns.xyz/benchmarkfcns/bealefcn.html) function. - pub fn beale(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "beale", - implementation: scaled_implementations::beale, - dimension, - domain: [-4.5, 4.5], - known_optimum: 0.0, - } - } - - /// The [Bird](http://benchmarkfcns.xyz/benchmarkfcns/birdfcn.html) function. - pub fn bird(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "bird", - implementation: scaled_implementations::bird, - dimension, - domain: [-2.0 * PI, 2.0 * PI], - known_optimum: -106.764537, - } - } - - /// The [Bohachevsky Nr. 1](http://benchmarkfcns.xyz/benchmarkfcns/bohachevskyn1fcn.html) function. - pub fn bohachevsky_n1(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "bohachevskyN1", - implementation: scaled_implementations::bohachevsky_n1, - dimension, - domain: [-100.0, 100.0], - known_optimum: 0.0, - } - } - - /// The [Bohachevsky Nr. 2](http://benchmarkfcns.xyz/benchmarkfcns/bohachevskyn2fcn.html) function. - pub fn bohachevsky_n2(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "bohachevskyN2", - implementation: scaled_implementations::bohachevsky_n2, - dimension, - domain: [-100.0, 100.0], - known_optimum: 0.0, - } - } - - /// The [Booth](http://benchmarkfcns.xyz/benchmarkfcns/boothfcn.html) function. - pub fn booth(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "booth", - implementation: scaled_implementations::booth, - dimension, - domain: [-10.0, 10.0], - known_optimum: 0.0, - } - } - - /// The [Brent](http://benchmarkfcns.xyz/benchmarkfcns/brentfcn.html) function. - pub fn brent(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "brent", - implementation: scaled_implementations::brent, - dimension, - domain: [-20.0, 0.0], - known_optimum: (-200.0_f64).exp(), - } - } - - /// The [Bukin Nr. 6](http://benchmarkfcns.xyz/benchmarkfcns/bukinn6fcn.html) function. - pub fn bukin_n6(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "bukinN6", - implementation: scaled_implementations::bukin_n6, - dimension, - domain: [-15.0, -5.0], - known_optimum: 0.0, - } - } - - /// The [Cross-in-Tray](http://benchmarkfcns.xyz/benchmarkfcns/crossintrayfcn.html) function. - pub fn cross_in_tray(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "crossInTray", - implementation: scaled_implementations::cross_in_tray, - dimension, - domain: [-10.0, 10.0], - known_optimum: -2.06261218, - } - } - - /// The [Deckkers-Aarts](http://benchmarkfcns.xyz/benchmarkfcns/deckkersaartsfcn.html) function. - pub fn deckkers_aarts(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "deckkersAarts", - implementation: scaled_implementations::deckkers_aarts, - dimension, - domain: [-20.0, 20.0], - known_optimum: -24771.09375, - } - } - - /// The [Drop-Wave](http://benchmarkfcns.xyz/benchmarkfcns/dropwavefcn.html) function. - pub fn drop_wave(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "dropWave", - implementation: scaled_implementations::drop_wave, - dimension, - domain: [-5.2, 5.2], - known_optimum: -1.0, - } - } - - /// The [Easom](http://benchmarkfcns.xyz/benchmarkfcns/easomfcn.html) function. - pub fn easom(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "easom", - implementation: scaled_implementations::easom, - dimension, - domain: [-100.0, 100.0], - known_optimum: -1.0, - } - } - - /// The [Egg Crate](http://benchmarkfcns.xyz/benchmarkfcns/eggcratefcn.html) function. - pub fn egg_crate(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "eggCrate", - implementation: scaled_implementations::egg_crate, - dimension, - domain: [-5.0, 5.0], - known_optimum: 0.0, - } - } - - /// The [Goldstein-Price](http://benchmarkfcns.xyz/benchmarkfcns/goldsteinpricefcn.html) function. - pub fn goldstein_price(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "goldsteinPrice", - implementation: scaled_implementations::goldstein_price, - dimension, - domain: [-2.0, 2.0], - known_optimum: 3.0, - } - } - - /// The [Gramacy & Lee](http://benchmarkfcns.xyz/benchmarkfcns/gramacyleefcn.html) function. - pub fn gramacy_lee(dimension: usize) -> Self { - assert_eq!(dimension, 1); - BenchmarkFunction { - name: "gramacyLee", - implementation: scaled_implementations::gramacy_lee, - dimension, - domain: [0.5, 2.5], - known_optimum: -0.869011134989500, - } - } - - /// The [Himmelblau](http://benchmarkfcns.xyz/benchmarkfcns/himmelblaufcn.html) function. - pub fn himmelblau(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "himmelblau", - implementation: scaled_implementations::himmelblau, - dimension, - domain: [-6.0, 6.0], - known_optimum: 0.0, - } - } - - /// The [Holder-Table](http://benchmarkfcns.xyz/benchmarkfcns/holdertablefcn.html) function. - pub fn holder_table(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "holderTable", - implementation: scaled_implementations::holder_table, - dimension, - domain: [-10.0, 10.0], - known_optimum: -19.2085, - } - } - - /// The [Keane](http://benchmarkfcns.xyz/benchmarkfcns/kealefcn.html) function. - pub fn keane(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "keane", - implementation: scaled_implementations::keane, - dimension, - domain: [0.0, 10.0], - known_optimum: -0.673667521146855, - } - } - - /// The [Leon](http://benchmarkfcns.xyz/benchmarkfcns/leonfcn.html) function. - pub fn leon(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "leon", - implementation: scaled_implementations::leon, - dimension, - domain: [0.0, 10.0], - known_optimum: 0.0, - } - } - - /// The [Levi Nr. 13](http://benchmarkfcns.xyz/benchmarkfcns/levin13fcn.html) function. - pub fn levi_n13(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "leviN13", - implementation: scaled_implementations::levi_n13, - dimension, - domain: [-10.0, 10.0], - known_optimum: 0.0, - } - } - - /// The [Matyas](http://benchmarkfcns.xyz/benchmarkfcns/matyasfcn.html) function. - pub fn matyas(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "matyas", - implementation: scaled_implementations::matyas, - dimension, - domain: [-10.0, 10.0], - known_optimum: 0.0, - } - } - - /// The [McCormick](http://benchmarkfcns.xyz/benchmarkfcns/mccormickfcn.html) function. - pub fn mccormick(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "mccormick", - implementation: scaled_implementations::mccormick, - dimension, - domain: [-1.5, 4.0], - known_optimum: -1.9133, - } - } - - /// The [Schaffer Nr. 1](http://benchmarkfcns.xyz/benchmarkfcns/schaffern1fcn.html) function. - pub fn schaffer_n1(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "schafferN1", - implementation: scaled_implementations::schaffer_n1, - dimension, - domain: [-100.0, 100.0], - known_optimum: 0.0, - } - } - - /// The [Schaffer Nr. 2](http://benchmarkfcns.xyz/benchmarkfcns/schaffern2fcn.html) function. - pub fn schaffer_n2(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "schafferN2", - implementation: scaled_implementations::schaffer_n2, - dimension, - domain: [-100.0, 100.0], - known_optimum: 0.0, - } - } - - /// The [Schaffer Nr. 3](http://benchmarkfcns.xyz/benchmarkfcns/schaffern3fcn.html) function. - pub fn schaffer_n3(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "schafferN3", - implementation: scaled_implementations::schaffer_n3, - dimension, - domain: [-100.0, 100.0], - known_optimum: 0.00156685, - } - } - - /// The [Schaffer Nr. 4](http://benchmarkfcns.xyz/benchmarkfcns/schaffern4fcn.html) function. - pub fn schaffer_n4(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "schafferN4", - implementation: scaled_implementations::schaffer_n4, - dimension, - domain: [-100.0, 100.0], - known_optimum: 0.292579, - } - } - - /// The [Three-Hump Camel](http://benchmarkfcns.xyz/benchmarkfcns/threehumpcamelfcn.html) function. - pub fn three_hump_camel(dimension: usize) -> Self { - assert_eq!(dimension, 2); - BenchmarkFunction { - name: "threeHumpCamel", - implementation: scaled_implementations::three_hump_camel, - dimension, - domain: [-5.0, 5.0], - known_optimum: 0.0, - } - } - - /// The [Wolfe](http://benchmarkfcns.xyz/benchmarkfcns/wolfefcn.html) function. - pub fn wolfe(dimension: usize) -> Self { - assert_eq!(dimension, 3); - BenchmarkFunction { - name: "wolfe", - implementation: scaled_implementations::wolfe, - dimension, - domain: [0.0, 2.0], - known_optimum: 0.0, - } - } -} - -/// The benchmark functions scaled to [-1.0, 1.0]. -pub mod scaled_implementations { - use std::f64::consts::PI; - //use std::intrinsics::sqrtf64; - - // general scaling from value within [-1,1] to input domain [min,max] of function - fn scale_domain(value: &f64, min: f64, max: f64) -> f64 { - (value + 1.0) / 2.0 * (max - min) + min - } - - /// Sphere function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...,0), here on input domain [-5.12,5.12] - pub fn sphere(x: &[f64]) -> f64 { - x.iter() - .map(|xi| scale_domain(xi, -5.12, 5.12)) - .map(|xi| xi * xi) - .sum() - } - - /// Rastrinin function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...,0), here on input domain [-5.12,5.12] - pub fn rastrigin(x: &[f64]) -> f64 { - let n = x.len() as f64; - 10.0 * n - + x.iter() - .map(|xi| scale_domain(xi, -5.12, 5.12)) - .map(|xi| xi * xi - 10.0 * (2.0 * PI * xi).cos()) - .sum::() - } - - /// Ackley function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...,0), here on input domain [-32,32] - pub fn ackley(x: &[f64]) -> f64 { - let a = 20.0; - let b = 0.2; - let c = 2.0 * PI; - - let n_inverse = 1.0 / x.len() as f64; - let squared_sum = x - .iter() - .map(|xi| scale_domain(xi, -32.0, 32.0)) - .map(|xi| xi.powi(2)) - .sum::(); - let cosine_sum = x - .iter() - .map(|xi| scale_domain(xi, -32.0, 32.0)) - .map(|xi| (c * xi).cos()) - .sum::(); - - a + (1.0_f64).exp() + (-a) * ((-b) * (n_inverse * squared_sum).sqrt()).exp() - - (n_inverse * cosine_sum).exp() - } - - /// Ackley Nr. 4 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: on 2-dimensional space -4.590101633799122 at (−1.51,−0.755), here on input domain [-35,35] - pub fn ackley_n4(x: &[f64]) -> f64 { - let mut sum = 0.0; - for i in 1..=(x.len() - 1) { - sum += (-0.2_f64).exp() - * ((scale_domain(&x[i - 1], -35.0, 35.0)).powi(2) - + (scale_domain(&x[i], -35.0, 35.0)).powi(2)) - .sqrt() - + 3.0 - * ((2.0 * (scale_domain(&x[i - 1], -35.0, 35.0))).cos() - + (2.0 * (scale_domain(&x[i], -35.0, 35.0))).sin()); - } - sum - } - - /// Alpine Nr. 1 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...,0), here on input domain [-10,10] - pub fn alpine_n1(x: &[f64]) -> f64 { - x.iter() - .map(|xi| scale_domain(xi, -10.0, 10.0)) - .map(|xi| (xi * xi.sin() + 0.1 * xi).abs()) - .sum::() - } - - /// Alpine Nr. 2 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: -2.808^n at (7.917,...,7.917) (minimisation by negation), here on input domain [0, 10] - pub fn alpine_n2(x: &[f64]) -> f64 { - -x.iter() - .map(|xi| scale_domain(xi, 0.0, 10.0)) - .map(|xi| xi.sqrt() * xi.sin()) - .product::() - } - - /// Brown function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...,0), here on input domain [-1,4] - pub fn brown(x: &[f64]) -> f64 { - let mut sum = 0.0; - for i in 1..=(x.len() - 1) { - sum += ((scale_domain(&x[i - 1], -1.0, 4.0)).powi(2)) - .powi(((scale_domain(&x[i], -1.0, 4.0)).powi(2) + 1.0) as i32) - + ((scale_domain(&x[i], -1.0, 4.0)).powi(2)) - .powi(((scale_domain(&x[i - 1], -1.0, 4.0)).powi(2) + 1.0) as i32); - } - sum - } - - /// Exponential function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: - 1.0 at (0,...,0) - pub fn exponential(x: &[f64]) -> f64 { - let sum = x.iter().map(|xi| xi.powi(2)).sum::(); - -((-0.5) * sum).exp() - } - - /// Griewank function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...,0), here on input domain [-600,600] - pub fn griewank(x: &[f64]) -> f64 { - let sum = x - .iter() - .map(|xi| scale_domain(xi, -600.0, 600.0)) - .map(|xi| xi.powi(2) / 4000.0) - .sum::(); - - let prod = x - .iter() - .map(|&xi| scale_domain(&xi, -600.0, 600.0)) - .enumerate() - .map(|(i, xi)| (xi / ((i as f64) + 1.0)).cos()) - .product::(); - - 1.0 + sum - prod - } - - /// Happy Cat function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (-1,...,-1), here on input domain [-2,2] - pub fn happy_cat(x: &[f64]) -> f64 { - let n = x.len() as f64; - let alpha = 1.0 / 8.0; - let norm = x - .iter() - .map(|xi| scale_domain(xi, -2.0, 2.0)) - .map(|xi| xi.powi(2)) - .sum::(); - - let sum = x.iter().map(|xi| scale_domain(xi, -2.0, 2.0)).sum::(); - - ((norm - n).powi(2)).powf(alpha) + (1.0 / n) * (0.5 * norm + sum) + 0.5 - } - - /// Periodic function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0.9 at (0,...,0), here on input domain [-10,10] - /// On , there is a typo in the mathematical definition! - pub fn periodic(x: &[f64]) -> f64 { - let sum = x - .iter() - .map(|xi| scale_domain(xi, -10.0, 10.0)) - .map(|xi| xi.powi(2)) - .sum::(); - - let sine_sum = x - .iter() - .map(|xi| scale_domain(xi, -10.0, 10.0)) - .map(|xi| (xi.sin()).powi(2)) - .sum::(); - - 1.0 + sine_sum - 0.1 * (-sum).exp() - } - - /// Powell Sum function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...,0) - pub fn powell_sum(x: &[f64]) -> f64 { - x.iter() - .enumerate() - .map(|(i, xi)| (xi.abs()).powi((i + 1) as i32)) - .sum::() - } - - /// Qing function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (+-(i).sqrt(),...,+-(i).sqrt()), here on input domain [-500,500] - pub fn qing(x: &[f64]) -> f64 { - x.iter() - .map(|xi| scale_domain(xi, -500.0, 500.0)) - .enumerate() - .map(|(i, xi)| (xi.powi(2) - (i as f64 + 1.0)).powi(2)) - .sum::() - } - - /// Ridge function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: -5 at (-5,0,...,0), here on input domain [-5,5] - pub fn ridge(x: &[f64]) -> f64 { - let d = 1.0; - let alpha = 0.5; - let first = scale_domain(&x[0], -5.0, 5.0); - - let sum = x[1..] - .iter() - .map(|xi| scale_domain(xi, -5.0, 5.0)) - .map(|xi| xi.powi(2)) - .sum::(); - - first + d * sum.powf(alpha) - } - - /// Rosenbrock function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (1,...1), here on input domain [-5,10] - pub fn rosenbrock(x: &[f64]) -> f64 { - let a = 1.0; - let b = 100.0; - let mut sum = 0.0; - - for i in 1..=(x.len() - 1) { - sum += b - * ((scale_domain(&x[i], -5.0, 10.0)) - - (scale_domain(&x[i - 1], -5.0, 10.0)).powi(2)) - .powi(2) - + (a - (scale_domain(&x[i - 1], -5.0, 10.0))).powi(2); - } - sum - } - - /// Salomon function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...0), here on input domain [-100,100] - pub fn salomon(x: &[f64]) -> f64 { - let sum = x - .iter() - .map(|xi| scale_domain(xi, -100.0, 100.0)) - .map(|xi| xi.powi(2)) - .sum::(); - - 1.0 - (2.0 * PI * sum.sqrt()).cos() + 0.1 * sum.sqrt() - } - - /// Schwefel 2.20 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...0), here on input domain [-100,100] - pub fn schwefel_220(x: &[f64]) -> f64 { - x.iter() - .map(|xi| scale_domain(xi, -100.0, 100.0)) - .map(|xi| xi.abs()) - .sum() - } - - /// Schwefel 2.21 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...0), here on input domain [-100,100] - pub fn schwefel_221(x: &[f64]) -> f64 { - let max_elem = x - .iter() - .map(|xi| (scale_domain(xi, -100.0, 100.0)).abs()) - .fold(f64::NEG_INFINITY, f64::max); - //.max_by(|a, b| a.total_cmp(b)); - - max_elem - } - - /// Schwefel 2.22 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...0), here on input domain [-100,100] - pub fn schwefel_222(x: &[f64]) -> f64 { - let sum = x - .iter() - .map(|xi| scale_domain(xi, -100.0, 100.0)) - .map(|xi| xi.abs()) - .sum::(); - - let prod = x - .iter() - .map(|xi| xi * 100.0) - .map(|xi| xi.abs()) - .product::(); - - sum + prod - } - - /// Schwefel 2.23 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...0), here on input domain [-10,10] - pub fn schwefel_223(x: &[f64]) -> f64 { - x.iter() - .map(|xi| scale_domain(xi, -10.0, 10.0)) - .map(|xi| xi.powi(10)) - .sum::() - } - - /// Schwefel function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (420.9687,...420.9687), here on input domain [-500,500] - pub fn schwefel(x: &[f64]) -> f64 { - let sum = x - .iter() - .map(|xi| scale_domain(xi, -500.0, 500.0)) - .map(|xi| xi * ((xi.abs()).sqrt()).sin()) - .sum::(); - - 418.9829 * (x.len() as f64) - sum - } - - /// Shubert Nr. 3 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: ~ -24.062499 at some points (-6.774576,-6.774576), ..., (5.791794,5.791794) here on input domain [-10,10] - pub fn shubert_n3(x: &[f64]) -> f64 { - let mut sum = 0.0; - for i in 1..=(x.len()) { - for j in 1..=5 { - sum += j as f64 - * ((j as f64 + 1.0) * (scale_domain(&x[i - 1], -10.0, 10.0)) + j as f64).sin(); - } - } - -sum - } - - /// Shubert Nr. 4 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: ~ -25.740858 at (-6.774576 + PI,-6.774576 + PI), here on input domain [-10,10] - pub fn shubert_n4(x: &[f64]) -> f64 { - let mut sum = 0.0; - for i in 1..=(x.len()) { - for j in 1..=5 { - sum += j as f64 - * ((j as f64 + 1.0) * (scale_domain(&x[i - 1], -10.0, 10.0)) + j as f64).cos(); - } - } - sum - } - - /// Shubert function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: ~ -186.7309 (18 optima), e.g. (−7.0835, 4.8580), here on input domain [-10,10] - pub fn shubert(x: &[f64]) -> f64 { - let mut prod = 1.0; - for i in 1..=(x.len()) { - let mut sum = 0.0; - for j in 1..=5 { - sum += ((j as f64 + 1.0) * (scale_domain(&x[i - 1], -10.0, 10.0)) + j as f64).cos(); - } - prod *= sum - } - prod - } - - /// Styblinski-Tang function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: -39.16599 * n at (-2.903534,...,-2.903534), here on input domain [-5,5] - pub fn styblinski_tang(x: &[f64]) -> f64 { - let sum = x - .iter() - .map(|xi| scale_domain(xi, -5.0, 5.0)) - .map(|xi| xi.powi(4) - 16.0 * xi.powi(2) + 5.0 * xi) - .sum::(); - - 0.5 * sum - } - - /// Sum Squares function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...,0), here on input domain [-10,10] - pub fn sum_squares(x: &[f64]) -> f64 { - x.iter() - .map(|xi| scale_domain(xi, -10.0, 10.0)) - .enumerate() - .map(|(i, xi)| i as f64 * xi.powi(2)) - .sum::() - } - - /// Xin-She Yang Nr. 2 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...,0), here on input domain [-2PI,2PI] - pub fn yang_n2(x: &[f64]) -> f64 { - let sum = x - .iter() - .map(|xi| scale_domain(xi, -2.0 * PI, 2.0 * PI)) - .map(|xi| xi.abs()) - .sum::(); - - let exp_sum = x - .iter() - .map(|xi| scale_domain(xi, -2.0 * PI, 2.0 * PI)) - .map(|xi| (xi.powi(2)).sin()) - .sum::(); - - sum * (-exp_sum).exp() - } - - /// Xin-She Yang Nr. 3 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: -1 at (0,...,0), here on input domain [-2PI,2PI] - pub fn yang_n3(x: &[f64]) -> f64 { - let beta = 15.0; - let m = 5.0; - - let beta_sum = x - .iter() - .map(|xi| scale_domain(xi, -2.0 * PI, 2.0 * PI)) - .map(|xi| (xi / beta).powf(2.0 * m)) - .sum::(); - - let sum = x - .iter() - .map(|xi| scale_domain(xi, -2.0 * PI, 2.0 * PI)) - .map(|xi| xi.powi(2)) - .sum::(); - - let prod = x - .iter() - .map(|xi| scale_domain(xi, -2.0 * PI, 2.0 * PI)) - .map(|xi| (xi.cos()).powi(2)) - .product::(); - - (-beta_sum).exp() - 2.0 * (-sum).exp() * prod - } - - /// Xin-She Yang Nr. 4 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: -1 at (0,...,0), here on input domain [-10,10] - pub fn yang_n4(x: &[f64]) -> f64 { - let inner_exp_sum = x - .iter() - .map(|xi| scale_domain(xi, -10.0, 10.0)) - .map(|xi| xi.powi(2)) - .sum::(); - - let outer_exp_sum = x - .iter() - .map(|xi| scale_domain(xi, -10.0, 10.0)) - .map(|xi| ((xi.abs()).sin()).powi(2)) - .sum::(); - - let sum = x - .iter() - .map(|xi| scale_domain(xi, -10.0, 10.0)) - .map(|xi| (xi.sin()).powi(2)) - .sum::(); - - (sum - (-inner_exp_sum).exp()) * (-outer_exp_sum).exp() - } - - /// Zakharov function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,...,0), here on input domain [-10,10] - pub fn zakharov(x: &[f64]) -> f64 { - let i_sum = x - .iter() - .map(|xi| scale_domain(xi, -10.0, 10.0)) - .enumerate() - .map(|(i, xi)| 0.5 * i as f64 * xi) - .sum::(); - - let sum = x - .iter() - .map(|xi| scale_domain(xi, -10.0, 10.0)) - .map(|xi| xi.powi(2)) - .sum::(); - - sum + (i_sum).powi(2) + (i_sum).powi(4) - } - - /// Ackley Nr. 2 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: -200 at (0,0), here on input domain [-32,32] - /// Defined only on 2-dimensional space. - pub fn ackley_n2(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -32.0, 32.0); - let b = scale_domain(&x[1], -32.0, 32.0); - -200.0 * (-0.2 * (a.powi(2) + b.powi(2)).sqrt()).exp() - } - - /// Ackley Nr. 3 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: −195.629028238419 at (±0.682584587365898,−0.36075325513719), here on input domain [-32,32] - /// Defined only on 2-dimensional space. - pub fn ackley_n3(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -32.0, 32.0); - let b = scale_domain(&x[1], -32.0, 32.0); - -200.0 * (-0.2 * (a.powi(2) + b.powi(2)).sqrt()).exp() - + 5.0 * ((3.0 * a).cos() + (3.0 * b.sin())).exp() - } - - /// Adjiman function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: −2.02181 at (2, 0.10578), here on input domain [-2,2] for x and [-1,1] for y - /// Defined only on 2-dimensional space. - pub fn adjiman(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -2.0, 2.0); - let b = scale_domain(&x[1], -1.0, 1.0); - a.cos() * b.sin() - (a / (b.powi(2) + 1.0)) - } - - /// Bartels Conn function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 1 at (0,0), here on input domain [-500,500] - /// Defined only on 2-dimensional space. - pub fn bartels_conn(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -500.0, 500.0); - let b = scale_domain(&x[1], -500.0, 500.0); - (a.powi(2) + b.powi(2) + (a * b)).abs() + (a.sin()).abs() + (b.cos()).abs() - } - - /// Beale function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (3,0.5), here on input domain [-4.5,4.5] - /// Defined only on 2-dimensional space. - pub fn beale(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -4.5, 4.5); - let b = scale_domain(&x[1], -4.5, 4.5); - (1.5 - a + (a * b)).powi(2) - + (2.25 - a + (a * b.powi(2))).powi(2) - + (2.625 - a + (a * b.powi(3))).powi(2) - } - - /// Bird function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: −106.764537 at (4.70104,3.15294) and (−1.58214,−3.13024), here on input domain [-2 * Pi,2 * Pi] - /// Defined only on 2-dimensional space. - pub fn bird(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -2.0 * PI, 2.0 * PI); - let b = scale_domain(&x[1], -2.0 * PI, 2.0 * PI); - a.sin() * ((1.0 - b.cos()).powi(2)).exp() - + b.cos() * ((1.0 - a.sin()).powi(2)).exp() - + (a - b).powi(2) - } - - /// Bohachevsky Nr. 1 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,0), here on input domain [-100,100] - /// Defined only on 2-dimensional space. - pub fn bohachevsky_n1(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -100.0, 100.0); - let b = scale_domain(&x[1], -100.0, 100.0); - a.powi(2) + (2.0 * b.powi(2)) - (0.3 * (3.0 * PI * a).cos()) - (0.4 * (4.0 * PI * b).cos()) - + 0.7 - } - - /// Bohachevsky Nr. 2 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,0), here on input domain [-100,100] - /// Defined only on 2-dimensional space. - pub fn bohachevsky_n2(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -100.0, 100.0); - let b = scale_domain(&x[1], -100.0, 100.0); - a.powi(2) + (2.0 * b.powi(2)) - (0.3 * (3.0 * PI * a).cos() * (4.0 * PI * b).cos()) + 0.3 - } - - /// Booth function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (1,3), here on input domain [-10,10] - /// Defined only on 2-dimensional space. - pub fn booth(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -10.0, 10.0); - let b = scale_domain(&x[1], -10.0, 10.0); - (a + (2.0 * b) - 7.0).powi(2) + ((2.0 * a) + b - 5.0).powi(2) - } - - /// Brent function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: e^(-200) at (-10,-10), here on input domain [-20,0] - /// Defined only on 2-dimensional space. - pub fn brent(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -20.0, 0.0); - let b = scale_domain(&x[1], -20.0, 0.0); - (a + 10.0).powi(2) + (b + 10.0).powi(2) + (-a.powi(2) - b.powi(2)).exp() - } - - /// Bukin Nr. 6 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (-10,1), here on input domain [-15,-5] for x and [-3,3] for y - /// Defined only on 2-dimensional space. - pub fn bukin_n6(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -15.0, -5.0); - let b = scale_domain(&x[1], -3.0, 3.0); - 100.0 * ((b - 0.01 * a.powi(2)).abs()).sqrt() + 0.01 * (a + 10.0).abs() - } - - /// Cross-in-Tray function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: - 2.06261218 at (±1.349406685353340,±1.349406608602084), here on input domain [-10,10] - /// Defined only on 2-dimensional space. - pub fn cross_in_tray(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -10.0, 10.0); - let b = scale_domain(&x[1], -10.0, 10.0); - -0.0001 - * ((a.sin() * b.sin() * ((100.0 - ((a.powi(2) + b.powi(2)).sqrt() / PI)).abs()).exp()) - .abs() - + 1.0) - .powf(0.1) - } - - /// Deckkers-Aarts function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: −24771.09375 at (0,±15), here on input domain [-20,20] - /// Defined only on 2-dimensional space. - pub fn deckkers_aarts(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -20.0, 20.0); - let b = scale_domain(&x[1], -20.0, 20.0); - (10.0_f64).powi(5) * a.powi(2) + b.powi(2) - (a.powi(2) + b.powi(2)).powi(2) - + (10.0_f64).powi(-5) * (a.powi(2) + b.powi(2)).powi(4) - } - - /// Drop-Wave function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: -1 at (0,0), here on input domain [-5.2,5.2] - /// Defined only on 2-dimensional space. - pub fn drop_wave(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -5.2, 5.2); - let b = scale_domain(&x[1], -5.2, 5.2); - -(1.0 + (12.0 * (a.powi(2) + b.powi(2)).sqrt()).cos()) - / (0.5 * (a.powi(2) + b.powi(2)) + 2.0) - } - - /// Easom function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: -1 at (π,π), here on input domain [-100,100] - /// Defined only on 2-dimensional space. - pub fn easom(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -100.0, 100.0); - let b = scale_domain(&x[1], -100.0, 100.0); - -a.cos() * b.cos() * (-(a - PI).powi(2) - (b - PI).powi(2)).exp() - } - - /// Egg Crate function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,0), here on input domain [-5,5] - /// Defined only on 2-dimensional space. - pub fn egg_crate(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -5.0, 5.0); - let b = scale_domain(&x[1], -5.0, 5.0); - a.powi(2) + b.powi(2) + 25.0 * ((a.sin()).powi(2) + (b.sin()).powi(2)) - } - - /// Goldstein-Price function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 3 at (0,-1), here on input domain [-2,2] - /// Defined only on 2-dimensional space. - pub fn goldstein_price(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -2.0, 2.0); - let b = scale_domain(&x[1], -2.0, 2.0); - (1.0 + (a + b + 1.0).powi(2) - * (19.0 - 14.0 * a + 3.0 * a.powi(2) - 14.0 * b + 6.0 * a * b + 3.0 * b.powi(2))) - * (30.0 - + (2.0 * a - 3.0 * b).powi(2) - * (18.0 - 32.0 * a + 12.0 * a.powi(2) + 48.0 * b - 36.0 * a * b - + 27.0 * b.powi(2))) - } - - /// Gramacy & Lee function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: −0.869011134989500 at 0.548563444114526, here on input domain [0.5,2.5] - /// Defined only on 1-dimensional space. - pub fn gramacy_lee(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 1); - let a = scale_domain(&x[0], 0.5, 2.5); - ((10.0 * PI * a).sin()) / (2.0 * a) + (a - 1.0).powi(4) - } - - /// Himmelblau function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (3,2), (−2.805118,3.283186), (−3.779310,−3.283186), (3.584458,−1.848126) here on input domain [-6,6] - /// Defined only on 2-dimensional space. - pub fn himmelblau(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -6.0, 6.0); - let b = scale_domain(&x[1], -6.0, 6.0); - (a.powi(2) + b - 11.0).powi(2) + (a + b.powi(2) - 7.0).powi(2) - } - - /// Holder-Table function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: −19.2085 at (±8.05502,±9.66459), here on input domain [-10,10] - /// Defined only on 2-dimensional space. - pub fn holder_table(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -10.0, 10.0); - let b = scale_domain(&x[1], -10.0, 10.0); - -(a.sin() * b.cos() * ((1.0 - (a.powi(2) + b.powi(2)).sqrt() / PI).abs()).exp()).abs() - } - - /// Keane function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: - 0.673667521146855 at (1.393249070031784,0) and (0,1.393249070031784), here on input domain [0, 10] - /// Defined only on 2-dimensional space. - pub fn keane(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], 0.0, 10.0); - let b = scale_domain(&x[1], 0.0, 10.0); - -(((a - b).sin()).powi(2) * ((a + b).sin()).powi(2)) / ((a.powi(2) + b.powi(2)).sqrt()) - } - - /// Leon function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (1,1), here on input domain [0, 10] - /// Defined only on 2-dimensional space. - pub fn leon(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], 0.0, 10.0); - let b = scale_domain(&x[1], 0.0, 10.0); - 100.0 * (b - a.powi(3)).powi(2) + (1.0 - a).powi(2) - } - - /// Levi Nr. 13 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (1,1), here on input domain [-10,10] - /// Defined only on 2-dimensional space. - pub fn levi_n13(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -10.0, 10.0); - let b = scale_domain(&x[1], -10.0, 10.0); - ((3.0 * PI * a).sin()).powi(2) - + (a - 1.0).powi(2) * (1.0 + ((3.0 * PI * b).sin()).powi(2)) - + (b - 1.0).powi(2) * (1.0 + ((2.0 * PI * b).sin()).powi(2)) - } - - /// Matyas function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,0), here on input domain [-10,10] - /// Defined only on 2-dimensional space. - pub fn matyas(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -10.0, 10.0); - let b = scale_domain(&x[1], -10.0, 10.0); - 0.26 * (a.powi(2) + b.powi(2)) - 0.48 * a * b - } - - /// McCormick function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: −1.9133 at (−0.547,−1.547), here on input domain [-1.5,4] for x and [-3,3] for y - /// Defined only on 2-dimensional space. - pub fn mccormick(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -1.5, 4.0); - let b = scale_domain(&x[1], -3.0, 3.0); - (a + b).sin() + (a - b).powi(2) - 1.5 * a + 2.5 * b + 1.0 - } - - /// Schaffer Nr. 1 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,0), here on input domain [-100,100] - /// Defined only on 2-dimensional space. - pub fn schaffer_n1(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -100.0, 100.0); - let b = scale_domain(&x[1], -100.0, 100.0); - 0.5 + ((((a.powi(2) + b.powi(2)).powi(2)).sin()).powi(2) - 0.5) - / (1.0 + 0.001 * (a.powi(2) + b.powi(2))).powi(2) - } - - /// Schaffer Nr. 2 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,0), here on input domain [-100,100] - /// Defined only on 2-dimensional space. - pub fn schaffer_n2(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -100.0, 100.0); - let b = scale_domain(&x[1], -100.0, 100.0); - 0.5 + (((a.powi(2) - b.powi(2)).sin()).powi(2) - 0.5) - / (1.0 + 0.001 * (a.powi(2) + b.powi(2))).powi(2) - } - - /// Schaffer Nr. 3 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0.00156685 at (0,1.253115), here on input domain [-100,100] - /// Defined only on 2-dimensional space. - pub fn schaffer_n3(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -100.0, 100.0); - let b = scale_domain(&x[1], -100.0, 100.0); - 0.5 + (((((a.powi(2) + b.powi(2)).abs()).cos()).sin()).powi(2) - 0.5) - / (1.0 + 0.001 * (a.powi(2) + b.powi(2))).powi(2) - } - - /// Schaffer Nr. 4 function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0.292579 at (0,1.253115), here on input domain [-100,100] - /// Defined only on 2-dimensional space. - pub fn schaffer_n4(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -100.0, 100.0); - let b = scale_domain(&x[1], -100.0, 100.0); - 0.5 + (((((a.powi(2) + b.powi(2)).abs()).sin()).cos()).powi(2) - 0.5) - / (1.0 + 0.001 * (a.powi(2) + b.powi(2))).powi(2) - } - - /// Three-Hump Camel function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,0), here on input domain [-5,5] - /// Defined only on 2-dimensional space. - pub fn three_hump_camel(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 2); - let a = scale_domain(&x[0], -5.0, 5.0); - let b = scale_domain(&x[1], -5.0, 5.0); - 2.0 * a.powi(2) - 1.05 * a.powi(4) + (a.powi(6) / 6.0) + a * b + b.powi(2) - } - - /// Wolfe function - /// - /// Scaled to [-1.0, 1.0] - /// - /// Optimum: 0 at (0,0,0), here on input domain [0, 2] - /// Defined only on 3-dimensional space. - pub fn wolfe(x: &[f64]) -> f64 { - debug_assert_eq!(x.len(), 3); - let a = scale_domain(&x[0], 0.0, 2.0); - let b = scale_domain(&x[1], 0.0, 2.0); - let c = scale_domain(&x[2], 0.0, 2.0); - 4.0 / 3.0 * (a.powi(2) + b.powi(2) - a * b).powf(0.75) + c - } -} diff --git a/src/problems/bmf/mod.rs b/src/problems/bmf/mod.rs deleted file mode 100644 index 10ffa741..00000000 --- a/src/problems/bmf/mod.rs +++ /dev/null @@ -1,170 +0,0 @@ -//! Collection of benchmark functions with corresponding tests. - -pub mod implementations; -#[cfg(test)] -pub mod tests; - -use crate::{ - framework::SingleObjective, - problems::{HasKnownOptimum, HasKnownTarget, LimitedVectorProblem, Problem, VectorProblem}, - state::common::EvaluatorInstance, -}; -use anyhow::anyhow; -use std::convert::TryFrom; - -/// Wraps the benchmark functions as [`Problem`]s. -/// -/// All functions have been scaled to [-1, 1]. -#[derive(Clone, Copy, serde::Serialize)] -pub struct BenchmarkFunction { - name: &'static str, - dimension: usize, - #[serde(skip)] - domain: [f64; 2], - - #[serde(skip)] - known_optimum: f64, - - #[serde(skip)] - implementation: Function, -} - -impl BenchmarkFunction { - fn evaluate_solution(&self, solution: &[f64]) -> SingleObjective { - (self.implementation)(solution).try_into().unwrap() - } -} - -impl Problem for BenchmarkFunction { - type Encoding = Vec; - type Objective = SingleObjective; - - fn name(&self) -> &str { - self.name - } - - fn default_evaluator<'a>(&self) -> EvaluatorInstance<'a, Self> { - EvaluatorInstance::functional(|problem, _state, individuals| { - for individual in individuals { - individual.evaluate(problem.evaluate_solution(individual.solution())); - } - }) - } -} - -impl VectorProblem for BenchmarkFunction { - type T = f64; - - fn dimension(&self) -> usize { - self.dimension - } -} - -impl LimitedVectorProblem for BenchmarkFunction { - fn range(&self, _dimension: usize) -> std::ops::Range { - 0.0..1.0 - } -} - -impl HasKnownTarget for BenchmarkFunction { - fn target_hit(&self, target: SingleObjective) -> bool { - float_eq::float_eq!(self.known_optimum, target.value(), ulps <= 10) - } -} - -impl HasKnownOptimum for BenchmarkFunction { - fn known_optimum(&self) -> SingleObjective { - self.known_optimum.try_into().unwrap() - } -} - -/// A benchmark function. -pub type Function = fn(&[f64]) -> f64; - -impl TryFrom<&str> for BenchmarkFunction { - type Error = anyhow::Error; - - fn try_from(value: &str) -> Result { - let err = || anyhow!("Invalid bmf format. Expected 'fn_name'."); - let mut parts = value.split(&['<', '>'][..]); - let name = parts.next().ok_or_else(err)?; - let dimension = parts.next().ok_or_else(err)?.parse::()?; - - match name.to_lowercase().as_str() { - "sphere" => Ok(BenchmarkFunction::sphere(dimension)), - "rastrigin" => Ok(BenchmarkFunction::rastrigin(dimension)), - "ackley" => Ok(BenchmarkFunction::ackley(dimension)), - "ackley_n4" => Ok(BenchmarkFunction::ackley_n4(dimension)), - "alpine_n1" => Ok(BenchmarkFunction::alpine_n1(dimension)), - "alpine_n2" => Ok(BenchmarkFunction::alpine_n2(dimension)), - "brown" => Ok(BenchmarkFunction::brown(dimension)), - "exponential" => Ok(BenchmarkFunction::exponential(dimension)), - "griewank" => Ok(BenchmarkFunction::griewank(dimension)), - "happy_cat" => Ok(BenchmarkFunction::happy_cat(dimension)), - "periodic" => Ok(BenchmarkFunction::periodic(dimension)), - "powell_sum" => Ok(BenchmarkFunction::powell_sum(dimension)), - "qing" => Ok(BenchmarkFunction::qing(dimension)), - "ridge" => Ok(BenchmarkFunction::ridge(dimension)), - "rosenbrock" => Ok(BenchmarkFunction::rosenbrock(dimension)), - "salomon" => Ok(BenchmarkFunction::salomon(dimension)), - "schwefel_220" => Ok(BenchmarkFunction::schwefel_220(dimension)), - "schwefel_221" => Ok(BenchmarkFunction::schwefel_221(dimension)), - "schwefel_222" => Ok(BenchmarkFunction::schwefel_222(dimension)), - "schwefel_223" => Ok(BenchmarkFunction::schwefel_223(dimension)), - "schwefel" => Ok(BenchmarkFunction::schwefel(dimension)), - "shubert_n3" => Ok(BenchmarkFunction::shubert_n3(dimension)), - "shubert_n4" => Ok(BenchmarkFunction::shubert_n4(dimension)), - "shubert" => Ok(BenchmarkFunction::shubert(dimension)), - "styblinski_tang" => Ok(BenchmarkFunction::styblinski_tang(dimension)), - "sum_squares" => Ok(BenchmarkFunction::sum_squares(dimension)), - "yang_n2" => Ok(BenchmarkFunction::yang_n2(dimension)), - "yang_n3" => Ok(BenchmarkFunction::yang_n3(dimension)), - "yang_n4" => Ok(BenchmarkFunction::yang_n4(dimension)), - "zakharov" => Ok(BenchmarkFunction::zakharov(dimension)), - "ackley_n2" => Ok(BenchmarkFunction::ackley_n2(dimension)), - "ackley_n3" => Ok(BenchmarkFunction::ackley_n3(dimension)), - "adjiman" => Ok(BenchmarkFunction::adjiman(dimension)), - "bartels_conn" => Ok(BenchmarkFunction::bartels_conn(dimension)), - "beale" => Ok(BenchmarkFunction::beale(dimension)), - "bird" => Ok(BenchmarkFunction::bird(dimension)), - "bohachevsky_n1" => Ok(BenchmarkFunction::bohachevsky_n1(dimension)), - "bohachevsky_n2" => Ok(BenchmarkFunction::bohachevsky_n2(dimension)), - "booth" => Ok(BenchmarkFunction::booth(dimension)), - "brent" => Ok(BenchmarkFunction::brent(dimension)), - "bukin_n6" => Ok(BenchmarkFunction::bukin_n6(dimension)), - "cross_in_tray" => Ok(BenchmarkFunction::cross_in_tray(dimension)), - "deckkers_aarts" => Ok(BenchmarkFunction::deckkers_aarts(dimension)), - "drop_wave" => Ok(BenchmarkFunction::drop_wave(dimension)), - "easom" => Ok(BenchmarkFunction::easom(dimension)), - "egg_crate" => Ok(BenchmarkFunction::egg_crate(dimension)), - "goldstein_price" => Ok(BenchmarkFunction::goldstein_price(dimension)), - "gramacy_lee" => Ok(BenchmarkFunction::gramacy_lee(dimension)), - "himmelblau" => Ok(BenchmarkFunction::himmelblau(dimension)), - "holder_table" => Ok(BenchmarkFunction::holder_table(dimension)), - "keane" => Ok(BenchmarkFunction::keane(dimension)), - "leon" => Ok(BenchmarkFunction::leon(dimension)), - "levi_n13" => Ok(BenchmarkFunction::levi_n13(dimension)), - "matyas" => Ok(BenchmarkFunction::matyas(dimension)), - "mccormick" => Ok(BenchmarkFunction::mccormick(dimension)), - "schaffer_n1" => Ok(BenchmarkFunction::schaffer_n1(dimension)), - "schaffer_n2" => Ok(BenchmarkFunction::schaffer_n2(dimension)), - "schaffer_n3" => Ok(BenchmarkFunction::schaffer_n3(dimension)), - "schaffer_n4" => Ok(BenchmarkFunction::schaffer_n4(dimension)), - "three_hump_camel" => Ok(BenchmarkFunction::three_hump_camel(dimension)), - "wolfe" => Ok(BenchmarkFunction::wolfe(dimension)), - _ => Err(anyhow!("Unknown benchmark funktion {}", name)), - } - } -} - -#[cfg(test)] -mod try_from_tests { - use super::*; - - #[test] - fn try_from_sphere() { - let bmf = BenchmarkFunction::try_from("sphere<20>").unwrap(); - assert_eq!(bmf.name, "sphere"); - assert_eq!(bmf.dimension, 20); - } -} diff --git a/src/problems/bmf/tests.rs b/src/problems/bmf/tests.rs deleted file mode 100644 index e46c320e..00000000 --- a/src/problems/bmf/tests.rs +++ /dev/null @@ -1,1600 +0,0 @@ -fn abs_tol() -> f64 { - 0.0000_1 -} - -fn r1st_tol() -> f64 { - 1.0 -} - -fn scale_domain(value: &f64, min: f64, max: f64) -> f64 { - 2.0 * (value - min) / (max - min) - 1.0 -} - -mod bmf_tests { - use crate::problems::bmf::{ - tests::{abs_tol, r1st_tol, scale_domain}, - *, - }; - use float_eq::*; - use proptest::prelude::*; - use std::f64::consts::PI; - - #[test] - fn test_optimum_sphere() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::sphere(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_sphere(x in prop::collection::vec(-1.0..1.0, 1..30)) { - let problem = BenchmarkFunction::sphere(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_rastrigin() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::rastrigin(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_rastrigin(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::rastrigin(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_ackley() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::ackley(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_ackley(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::ackley(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_ackley_n4() { - // test for known optimum - let dimension_known = 2; - let problem = BenchmarkFunction::ackley_n4(dimension_known); - let x1 = scale_domain(&-1.51, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&-0.755, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - // Optimum only known for 2 dimensions! - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_ackley_n4(x in prop::collection::vec(-1.0f64..1.0f64, 1..2)) { - let problem = BenchmarkFunction::ackley_n4(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_alpine_n1() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::alpine_n1(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_alpine_n1(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::alpine_n1(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_alpine_n2() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::alpine_n2(dimension); - let xi = scale_domain(&7.917, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_alpine_n2(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::alpine_n2(x.len()); - // let random_fitness = problem.evaluate(&x); - // for now, in 1D calculated optimum is truncated to 3 decimal places - let random_fitness = match x.len() { - 1 => (problem.evaluate_solution(&x).value() * 1000.0).trunc() / 1000.0, - _ => problem.evaluate_solution(&x).value(), - }; - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_brown() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::brown(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_brown(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::brown(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_exponential() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::exponential(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_exponential(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::exponential(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_griewank() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::griewank(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_griewank(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::griewank(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_happy_cat() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::happy_cat(dimension); - let xi = scale_domain(&-1.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_happy_cat(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::happy_cat(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_periodic() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::periodic(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_periodic(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::periodic(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_powell_sum() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::powell_sum(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_powell_sum(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::powell_sum(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_qing() { - // test for known optimum - let dimension = 5; - let problem = BenchmarkFunction::qing(dimension); - let optimum_position = vec![ - (1.0_f64).sqrt() / 500.0, - (2.0_f64).sqrt() / 500.0, - (3.0_f64).sqrt() / 500.0, - (4.0_f64).sqrt() / 500.0, - (5.0_f64).sqrt() / 500.0, - ]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_qing(x in prop::collection::vec(-1.0f64..1.0f64, 5)) { - let problem = BenchmarkFunction::qing(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_ridge() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::ridge(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let mut optimum_position = vec![-5.0 / 5.0]; - let mut opt_rest = vec![xi; dimension - 1]; - optimum_position.append(&mut opt_rest); - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_ridge(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::ridge(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_rosenbrock() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::rosenbrock(dimension); - let xi = scale_domain(&1.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_rosenbrock(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::rosenbrock(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_salomon() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::salomon(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_salomon(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::salomon(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_schwefel_220() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::schwefel_220(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_schwefel_220(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::schwefel_220(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_schwefel_221() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::schwefel_221(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_schwefel_221(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::schwefel_221(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_schwefel_222() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::schwefel_222(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_schwefel_222(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::schwefel_222(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_schwefel_223() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::schwefel_223(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_schwefel_223(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::schwefel_223(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_schwefel() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::schwefel(dimension); - let xi = scale_domain(&420.9687, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= 0.00_1, - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_schwefel(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::schwefel(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_shubert_n3() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::shubert_n3(dimension); - // several optima, e.g. (-6.774576,-6.774576) - let xi = scale_domain(&-6.774576, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_shubert_n3(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::shubert_n3(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_shubert_n4() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::shubert_n4(dimension); - // several optima, positions of shubert_n3 + PI as cos instead of sin - let xi = scale_domain(&(-6.774576 + PI), problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_shubert_n4(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::shubert_n4(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_shubert() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::shubert(dimension); - let x1 = scale_domain(&-7.0835, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&4.8580, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_shubert(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::shubert(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_styblinski_tang() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::styblinski_tang(dimension); - let xi = scale_domain(&-2.903534, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_styblinski_tang(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::styblinski_tang(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_sum_sqares() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::sum_squares(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_sum_squares(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::sum_squares(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_yang_n2() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::yang_n2(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_yang_n2(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::yang_n2(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_yang_n3() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::yang_n3(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_yang_n3(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::yang_n3(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_yang_n4() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::yang_n4(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_yang_n4(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::yang_n4(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_zakharov() { - // test for known optimum - let dimension = 10; - let problem = BenchmarkFunction::zakharov(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_zakharov(x in prop::collection::vec(-1.0f64..1.0f64, 1..30)) { - let problem = BenchmarkFunction::zakharov(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - // from here on: non-n-dimensional functions - #[test] - fn test_optimum_ackley_n2() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::ackley_n2(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_ackley_n2(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::ackley_n2(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_ackley_n3() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::ackley_n3(dimension); - let x1 = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&-0.4, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_ackley_n3(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::ackley_n3(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_adjiman() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::adjiman(dimension); - // domain for x and y differs - let x1 = scale_domain(&2.0, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&0.10578, -1.0, 1.0); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_adjiman(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::adjiman(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_bartels_conn() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::bartels_conn(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_bartels_conn(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::bartels_conn(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_beale() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::beale(dimension); - let x1 = scale_domain(&3.0, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&0.5, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_beale(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::beale(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_bird() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::bird(dimension); - let x1 = scale_domain(&4.70104, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&3.15294, problem.domain()[0], problem.domain()[1]); - let x3 = scale_domain(&-1.58214, problem.domain()[0], problem.domain()[1]); - let x4 = scale_domain(&-3.13024, problem.domain()[0], problem.domain()[1]); - let optimum_position1 = vec![x1, x2]; - let optimum_position2 = vec![x3, x4]; - let optimum_fitness1 = problem.evaluate_solution(&optimum_position1).value(); - let optimum_fitness2 = problem.evaluate_solution(&optimum_position2).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness1, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness2, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_bird(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::bird(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_bohachevsky_n1() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::bohachevsky_n1(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_bohachevsky_n1(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::bohachevsky_n1(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_bohachevsky_n2() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::bohachevsky_n2(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_bohachevsky_n2(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::bohachevsky_n2(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_booth() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::booth(dimension); - let x1 = scale_domain(&1.0, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&3.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_booth(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::booth(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_brent() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::brent(dimension); - let xi = scale_domain(&-10.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_brent(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::brent(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_bukin_n6() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::bukin_n6(dimension); - // x and y have different domains - let x1 = scale_domain(&-10.0, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&1.0, -3.0, 3.0); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_bukin_n6(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::bukin_n6(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_cross_in_tray() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::cross_in_tray(dimension); - let x1 = scale_domain( - &1.349_406_685_353_34, - problem.domain()[0], - problem.domain()[1], - ); - let x2 = scale_domain( - &1.349_406_608_602_084, - problem.domain()[0], - problem.domain()[1], - ); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_cross_in_tray(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::cross_in_tray(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_deckkers_aarts() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::deckkers_aarts(dimension); - let x1 = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&15.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_deckkers_aarts(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::deckkers_aarts(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_drop_wave() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::drop_wave(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_drop_wave(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::drop_wave(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_easom() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::easom(dimension); - let xi = scale_domain(&PI, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_easom(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::easom(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_egg_crate() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::egg_crate(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_egg_crate(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::egg_crate(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_goldstein_price() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::goldstein_price(dimension); - let x1 = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&-1.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_goldstein_price(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::goldstein_price(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_gramacy_lee() { - // test for known optimum - let dimension = 1; - let problem = BenchmarkFunction::gramacy_lee(dimension); - let xi = scale_domain(&0.548563444114526, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_gramacy_lee(x in prop::collection::vec(-1.0f64..1.0f64, 1)) { - let problem = BenchmarkFunction::gramacy_lee(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_himmelblau() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::himmelblau(dimension); - let x1 = scale_domain(&3.0, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&2.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_himmelblau(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::himmelblau(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_holder_table() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::holder_table(dimension); - let x1 = scale_domain(&8.05502, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&9.66459, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_holder_table(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::holder_table(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_keane() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::keane(dimension); - let x1 = scale_domain(&1.393249070031784, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_keane(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::keane(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_leon() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::leon(dimension); - let xi = scale_domain(&1.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_leon(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::leon(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_levi_n13() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::levi_n13(dimension); - let xi = scale_domain(&1.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_levi_n13(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::levi_n13(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_matyas() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::matyas(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_matyas(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::matyas(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_mccormick() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::mccormick(dimension); - // different domains for x and y - let x1 = scale_domain(&-0.547, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&-1.547, -3.0, 3.0); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_mccormick(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::mccormick(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_schaffer_n1() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::schaffer_n1(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_schaffer_n1(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::schaffer_n1(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_schaffer_n2() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::schaffer_n2(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_schaffer_n2(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::schaffer_n2(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_schaffer_n3() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::schaffer_n3(dimension); - let x1 = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&1.253115, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_schaffer_n3(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::schaffer_n3(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_schaffer_n4() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::schaffer_n4(dimension); - let x1 = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let x2 = scale_domain(&1.253115, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![x1, x2]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_schaffer_n4(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::schaffer_n4(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_three_hump_camel() { - // test for known optimum - let dimension = 2; - let problem = BenchmarkFunction::three_hump_camel(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_three_hump_camel(x in prop::collection::vec(-1.0f64..1.0f64, 2)) { - let problem = BenchmarkFunction::three_hump_camel(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } - - #[test] - fn test_optimum_wolfe() { - // test for known optimum - let dimension = 3; - let problem = BenchmarkFunction::wolfe(dimension); - let xi = scale_domain(&0.0, problem.domain()[0], problem.domain()[1]); - let optimum_position = vec![xi; dimension]; - let optimum_fitness = problem.evaluate_solution(&optimum_position).value(); - assert_float_eq!( - problem.known_optimum(), - optimum_fitness, - abs <= abs_tol(), - r1st <= r1st_tol() - ); - } - - proptest! { - #[test] - fn test_random_input_wolfe(x in prop::collection::vec(-1.0f64..1.0f64, 3)) { - let problem = BenchmarkFunction::wolfe(x.len()); - let random_fitness = problem.evaluate_solution(&x).value(); - prop_assert!(random_fitness >= problem.known_optimum()); - } - } -} diff --git a/src/problems/coco/functions.rs b/src/problems/coco/functions.rs deleted file mode 100644 index 63f8d228..00000000 --- a/src/problems/coco/functions.rs +++ /dev/null @@ -1,167 +0,0 @@ -use std::{f64::consts::PI, ops::Range}; - -#[cfg(doc)] -use super::transformations::input; - -pub const DEFAULT_DOMAIN: Range = -5.0..5.0; -const DEFAULT_OPTIMUM: f64 = 0.0; - -pub struct FunctionObject { - pub evaluate: fn(x: &[f64]) -> f64, - pub best_parameter: fn(dim: usize) -> Vec, - pub domain: fn(dim: usize) -> Vec>, - pub best_value: fn(dim: usize) -> f64, -} -impl FunctionObject { - pub fn new() -> Self { - FunctionObject { - evaluate: F::evaluate, - best_parameter: F::best_parameter, - domain: F::domain, - best_value: F::best_value, - } - } -} -impl From for FunctionObject { - fn from(_: F) -> Self { - FunctionObject::new::() - } -} - -pub trait Function { - fn evaluate(x: &[f64]) -> f64; - - fn best_parameter(dim: usize) -> Vec { - vec![DEFAULT_OPTIMUM; dim] - } - - fn domain(dim: usize) -> Vec> { - vec![DEFAULT_DOMAIN; dim] - } - - fn best_value(dim: usize) -> f64 { - Self::evaluate(&Self::best_parameter(dim)) - } -} - -/// Sphere -pub struct Sphere; -impl Function for Sphere { - fn evaluate(x: &[f64]) -> f64 { - x.iter().map(|xi| xi * xi).sum() - } -} - -pub struct Ellipsoid; -impl Function for Ellipsoid { - fn evaluate(x: &[f64]) -> f64 { - let condition = 1.0e6f64; - let n = x.len() as f64; - - x.iter() - .enumerate() - .map(|(i, xi)| (i as f64, *xi)) - .map(|(i, xi)| condition.powf(i / (n - 1.0)) * xi * xi) - .sum() - } -} - -/// Rastrigin -pub struct Rastrigin; -impl Function for Rastrigin { - fn evaluate(x: &[f64]) -> f64 { - let num = x.len() as f64; - let sum1 = x.iter().map(|&xi| (2.0 * PI * xi).cos()).sum::(); - let sum2 = x.iter().map(|&xi| xi * xi).sum::(); - - 10.0 * (num - sum1) + sum2 - } -} - -/// Büche Rastrigin -/// -/// This is the same as [Rastrigin] -pub use Rastrigin as BuecheRastrigin; - -/// Linear Slope -/// -/// # Important -/// This deviates from coco. Unlike coco's implementation -/// the optimum is always at 5^n. It will perform the same -/// as in Coco once paired with a [input::Scale] filled with -/// the signs of the optimum, so either -1 or +1. -pub struct LinearSlope; -impl Function for LinearSlope { - fn evaluate(x: &[f64]) -> f64 { - let n = x.len() as f64; - - x.iter() - .enumerate() - .map(|(i, xi)| (i as f64, *xi)) - .map(|(i, xi)| (10.0f64.powf(i / (n - 1.0)), xi)) - .map(|(si, xi)| si * (5.0 - f64::min(5.0, xi))) - .sum::() - } -} -#[cfg(test)] -#[test] -fn linear_slope_optimum_check() { - let input = &[5.0; 10]; - let output = LinearSlope::evaluate(input); - float_eq::assert_float_eq!(output, 0.0, abs <= 0.0); -} - -pub struct Rosenbrock; -impl Function for Rosenbrock { - fn evaluate(x: &[f64]) -> f64 { - let sum1 = x - .iter() - .zip(x.iter().skip(1)) - .map(|(xi, xj)| xi * xi - xj) - .map(|xi| xi * xi) - .sum::(); - let sum2 = x.iter().map(|xi| xi - 1.0).map(|xi| xi * xi).sum::(); - - 100.0 * sum1 + sum2 - } -} - -/// Attractive Sector -/// -/// Important: This deviates from coco! -// This is what it is supposed to be: -// ```c -// static double f_attractive_sector_raw(const double *x, -// const size_t number_of_variables, -// f_attractive_sector_data_t *data) { -// size_t i; -// double result; -// -// if (coco_vector_contains_nan(x, number_of_variables)) -// return NAN; -// -// result = 0.0; -// for (i = 0; i < number_of_variables; ++i) { -// if (data->xopt[i] * x[i] > 0.0) { -// result += 100.0 * 100.0 * x[i] * x[i]; -// } else { -// result += x[i] * x[i]; -// } -// } -// return result; -// } -// ``` -pub struct AttractiveSector; -impl Function for AttractiveSector { - fn evaluate(x: &[f64]) -> f64 { - fn factor(xi: f64) -> f64 { - if xi < 0.0 { - 10000.0 - } else { - 1.0 - } - } - - x.iter().map(|&xi| factor(xi) * xi * xi).sum() - } -} diff --git a/src/problems/coco/mod.rs b/src/problems/coco/mod.rs deleted file mode 100644 index ccd1b43c..00000000 --- a/src/problems/coco/mod.rs +++ /dev/null @@ -1,162 +0,0 @@ -//! Numerical Black-Box Optimization Benchmarking Framework -//! -//! Implementation is base on - -use std::{mem, ops::Range}; - -pub mod functions; -pub mod suits; -pub mod transformations; - -use functions::FunctionObject; - -pub trait InputTransformation: Send + Sync { - /// Transform the function input. - fn apply(&self, x: &[f64], out: &mut [f64]); - - /// Reverse the function input transformation. - /// - /// # Important - /// This is not fully supported by Coco! - /// - /// Coco does implement this for some transformations, - /// but most of the complex onces do not implement this. - /// Without this being implemented, we won't know the - /// optimal parameters of a transformed function. - fn reverse(&self, x: &[f64], out: &mut [f64]) { - let _ = (x, out); - unimplemented!("Coco officially does not support this.") - } -} - -pub trait OutputTransformation: Send + Sync { - /// Transform the function output. - fn apply(&self, x: f64) -> f64; - - /// Reverse the function output transformation. - fn reverse(&self, x: f64) -> f64; -} - -pub struct Problem { - /// Transformations applied before evaluation - pub input_transformations: Vec>, - /// The base function - pub function: FunctionObject, - /// Transformations applied after evaluation - pub output_transformations: Vec>, -} -impl Problem { - pub fn evaluate<'a>(&self, mut x: &'a mut [f64], mut buffer: &'a mut [f64]) -> f64 { - debug_assert_eq!(x.len(), buffer.len()); - - for transformation in &self.input_transformations { - transformation.apply(x, buffer); - mem::swap(&mut x, &mut buffer); - } - - let mut y = (self.function.evaluate)(x); - - for transformation in &self.output_transformations { - y = transformation.apply(y); - } - - y - } -} - -#[derive(serde::Serialize)] -pub struct CocoInstance { - #[serde(skip)] - problem: Problem, - suite: &'static str, - function: usize, - instance: usize, - dimension: usize, -} -impl CocoInstance { - pub fn format_name(&self) -> String { - format!("f{}_d{}_i{}", self.function, self.dimension, self.instance) - } - - pub fn domain(&self) -> Vec> { - // TODO: cache this - (self.problem.function.domain)(self.dimension) - } - - pub fn best_value(&self) -> f64 { - // TODO: cache this - let mut y = (self.problem.function.best_value)(self.dimension); - - for transformation in &self.problem.output_transformations { - y = transformation.apply(y); - } - - y - } -} -impl crate::problems::Problem for CocoInstance { - type Encoding = Vec; - type Objective = crate::framework::SingleObjective; - - fn evaluate_solution(&self, solution: &Self::Encoding) -> Self::Objective { - debug_assert_eq!(self.dimension, solution.len()); - let b1 = &mut solution.clone(); - let b2 = &mut solution.clone(); - self.problem.evaluate(b1, b2).try_into().unwrap() - } - - fn name(&self) -> &str { - "coco" - } -} -impl crate::problems::VectorProblem for CocoInstance { - type T = f64; - - fn dimension(&self) -> usize { - self.dimension - } -} -impl crate::problems::LimitedVectorProblem for CocoInstance { - fn range(&self, dimension: usize) -> Range { - self.domain()[dimension].clone() - } -} - -impl crate::problems::HasKnownOptimum for CocoInstance { - fn known_optimum(&self) -> Self::Objective { - self.best_value().try_into().unwrap() - } -} - -#[cfg(test)] -mod tests { - use float_eq::assert_float_eq; - - use crate::problems::coco::{ - functions, - transformations::{input, output}, - Problem, - }; - - #[test] - fn create_permutated_sphere() { - let problem = Problem { - input_transformations: vec![input::Permutation::new(vec![2, 1, 0])], - function: functions::Sphere.into(), - output_transformations: vec![], - }; - let out = problem.evaluate(&mut [1.0, 2.0, 3.0], &mut [0.0, 0.0, 0.0]); - assert_float_eq!(out, 1.0 + 4.0 + 9.0, abs <= 0.0); - } - - #[test] - fn translate_sphere() { - let problem = Problem { - input_transformations: vec![input::Translate::new(vec![1.0, 1.0, 1.0])], - function: functions::Sphere.into(), - output_transformations: vec![output::Translate::new(5.0)], - }; - let out = problem.evaluate(&mut [1.0, 1.0, 1.0], &mut [0.0, 0.0, 0.0]); - assert_float_eq!(out, 5.0, abs <= 0.0); - } -} diff --git a/src/problems/coco/suits/bbob.rs b/src/problems/coco/suits/bbob.rs deleted file mode 100644 index 192a21bd..00000000 --- a/src/problems/coco/suits/bbob.rs +++ /dev/null @@ -1,180 +0,0 @@ -#![allow(unused_variables, dead_code)] - -use crate::problems::coco::{suits::Suite, CocoInstance}; -use std::ops::RangeInclusive; - -mod util_2009; - -pub enum Years { - Y2009 = 2009, - Y2010 = 2010, - Y2012 = 2012, - Y2013 = 2013, - Y2015 = 2015, - Y2016 = 2016, - Y2017 = 2017, - Y2018 = 2018, - Y2021 = 2021, -} - -impl Years { - pub fn current() -> Self { - Years::Y2021 - } -} - -pub fn new(year: Years) -> Suite { - Suite::new( - flatten_ranges(&[1..=24]), - instances_by_year(year), - vec![2, 3, 5, 10, 20, 40], - generator, - ) -} - -fn flatten_ranges(ranges: &[RangeInclusive]) -> Vec { - let mut list = Vec::new(); - - for range in ranges { - for i in range.clone() { - list.push(i); - } - } - - list -} - -fn instances_by_year(year: Years) -> Vec { - let ranges: &[_] = match year { - Years::Y2009 => &[1..=5, 1..=5, 1..=5], - Years::Y2010 => &[1..=15], - Years::Y2012 => &[1..=5, 21..=30], - Years::Y2013 => &[1..=5, 31..=40], - Years::Y2015 => &[1..=5, 41..=50], - Years::Y2016 => &[1..=5, 51..=60], - Years::Y2017 => &[1..=5, 61..=70], - Years::Y2018 => &[1..=5, 71..=80], - Years::Y2021 => &[1..=5, 91..=100], - }; - - flatten_ranges(ranges) -} - -fn generator(function: usize, instance: usize, dimension: usize) -> CocoInstance { - assert_eq!(instance, 1, "Toy suite only contains one instance"); - - let rseed = function + 10000 * instance; - let rseed_3 = 3 + 10000 * instance; - let rseed_17 = 17 + 10000 * instance; - - let problem = match function { - 0 => panic!("Suite functions start at 1, 0 was requested"), - 1 => functions::sphere(function, instance, dimension, rseed), - 2 => functions::ellipsoid(function, instance, dimension, rseed), - 3 => functions::rastrigin(function, instance, dimension, rseed), - 4 => functions::bueche_rastrigin(function, instance, dimension, rseed_3), - 5 => functions::linear_slope(function, instance, dimension, rseed), - 6..=24 => todo!("These are not implemented yet"), - _ => panic!( - "BBOB suite only contains 24 functions ({} was requested)", - function - ), - }; - - CocoInstance { - suite: "BBOB", - problem, - function, - instance, - dimension, - } -} - -mod functions { - #![allow(clippy::let_and_return)] - - use super::util_2009; - use crate::problems::coco::{ - functions, - transformations::{input, output}, - Problem, - }; - - pub fn sphere(function: usize, dimension: usize, instance: usize, rseed: usize) -> Problem { - let xopt = util_2009::compute_xopt(rseed, dimension); - let fopt = util_2009::compute_fopt(function, instance); - - Problem { - input_transformations: vec![input::Translate::new(xopt)], - function: functions::Sphere.into(), - output_transformations: vec![output::Translate::new(fopt)], - } - } - - pub fn ellipsoid(function: usize, dimension: usize, instance: usize, rseed: usize) -> Problem { - let xopt = util_2009::compute_xopt(rseed, dimension); - let fopt = util_2009::compute_fopt(function, instance); - - Problem { - input_transformations: vec![input::Oscillate::new(), input::Translate::new(xopt)], - function: functions::Ellipsoid.into(), - output_transformations: vec![output::Translate::new(fopt)], - } - } - - pub fn rastrigin(function: usize, dimension: usize, instance: usize, rseed: usize) -> Problem { - let xopt = util_2009::compute_xopt(rseed, dimension); - let fopt = util_2009::compute_fopt(function, instance); - - Problem { - input_transformations: vec![ - input::Condition::new(10.0), - input::Asymmetric::new(0.2), - input::Oscillate::new(), - input::Translate::new(xopt), - ], - function: functions::Rastrigin.into(), - output_transformations: vec![output::Translate::new(fopt)], - } - } - - pub fn bueche_rastrigin( - function: usize, - dimension: usize, - instance: usize, - rseed: usize, - ) -> Problem { - let xopt = util_2009::compute_xopt(rseed, dimension); - let fopt = util_2009::compute_fopt(function, instance); - - Problem { - input_transformations: vec![ - input::Brs::new(), - input::Oscillate::new(), - input::Translate::new(xopt), - ], - function: functions::Rastrigin.into(), - output_transformations: vec![output::Translate::new(fopt)], - } - } - - pub fn linear_slope( - function: usize, - dimension: usize, - instance: usize, - rseed: usize, - ) -> Problem { - let mut xopt = util_2009::compute_xopt(rseed, dimension); - let fopt = util_2009::compute_fopt(function, instance); - - for xi in &mut xopt { - *xi = if *xi >= 0.0 { 1.0 } else { -1.0 }; - } - - Problem { - input_transformations: vec![input::Scale::new(xopt)], - function: functions::LinearSlope.into(), - output_transformations: vec![output::Translate::new(fopt)], - } - } -} diff --git a/src/problems/coco/suits/bbob/legacy_code.c b/src/problems/coco/suits/bbob/legacy_code.c deleted file mode 100644 index ca1788cf..00000000 --- a/src/problems/coco/suits/bbob/legacy_code.c +++ /dev/null @@ -1,141 +0,0 @@ -/** - * @file suite_bbob_legacy_code.c - * @brief Legacy code from BBOB2009 required to replicate the 2009 functions. - * - * All of this code should only be used by the suite_bbob2009 functions to provide compatibility to the - * legacy code. New test beds should strive to use the new COCO facilities for random number generation etc. - */ - -#include -#include -#include - -/** extract from coco.h */ -static const double coco_pi = 3.14159265358979323846; - -/** @brief Maximal dimension used in BBOB2009. */ -#define SUITE_BBOB2009_MAX_DIM 40 - -/** @brief Computes the minimum of the two values. */ -static double bbob2009_fmin(double a, double b) { - return (a < b) ? a : b; -} - -/** @brief Computes the maximum of the two values. */ -static double bbob2009_fmax(double a, double b) { - return (a > b) ? a : b; -} - -/** @brief Rounds the given value. */ -static double bbob2009_round(double x) { - return floor(x + 0.5); -} - -/** - * @brief Generates N uniform random numbers using inseed as the seed and stores them in r. - */ -static void bbob2009_unif(double *r, size_t N, long inseed) { - /* generates N uniform numbers with starting seed */ - long aktseed; - long tmp; - long rgrand[32]; - long aktrand; - long i; - - if (inseed < 0) - inseed = -inseed; - if (inseed < 1) - inseed = 1; - aktseed = inseed; - for (i = 39; i >= 0; i--) { - tmp = (int) floor((double) aktseed / (double) 127773); - aktseed = 16807 * (aktseed - tmp * 127773) - 2836 * tmp; - if (aktseed < 0) - aktseed = aktseed + 2147483647; - if (i < 32) - rgrand[i] = aktseed; - } - aktrand = rgrand[0]; - for (i = 0; i < (long) N; i++) { - tmp = (int) floor((double) aktseed / (double) 127773); - aktseed = 16807 * (aktseed - tmp * 127773) - 2836 * tmp; - if (aktseed < 0) - aktseed = aktseed + 2147483647; - tmp = (int) floor((double) aktrand / (double) 67108865); - aktrand = rgrand[tmp]; - rgrand[tmp] = aktseed; - r[i] = (double) aktrand / 2.147483647e9; - if (r[i] == 0.) { - r[i] = 1e-99; - } - } - return; -} - -/** - * @brief Generates N Gaussian random numbers using the given seed and stores them in g. - */ -static void bbob2009_gauss(double *g, const size_t N, const long seed) { - size_t i; - double uniftmp[6000]; - assert(2 * N < 6000); - bbob2009_unif(uniftmp, 2 * N, seed); - - for (i = 0; i < N; i++) { - g[i] = sqrt(-2 * log(uniftmp[i])) * cos(2 * coco_pi * uniftmp[N + i]); - if (g[i] == 0.) - g[i] = 1e-99; - } - return; -} - -/** - * @brief Randomly computes the location of the global optimum. - */ -void bbob2009_compute_xopt(double *xopt, const long seed, const size_t DIM) { - size_t i; - bbob2009_unif(xopt, DIM, seed); - for (i = 0; i < DIM; i++) { - xopt[i] = 8 * floor(1e4 * xopt[i]) / 1e4 - 4; - if (xopt[i] == 0.0) - xopt[i] = -1e-5; - } -} - -/** - * @brief Randomly chooses the objective offset for the given function and instance. - */ -double bbob2009_compute_fopt(const size_t function, const size_t instance) { - long rseed, rrseed; - double gval, gval2; - - if (function == 4) - rseed = 3; - else if (function == 18) - rseed = 17; - else if (function == 101 || function == 102 || function == 103 || function == 107 - || function == 108 || function == 109) - rseed = 1; - else if (function == 104 || function == 105 || function == 106 || function == 110 - || function == 111 || function == 112) - rseed = 8; - else if (function == 113 || function == 114 || function == 115) - rseed = 7; - else if (function == 116 || function == 117 || function == 118) - rseed = 10; - else if (function == 119 || function == 120 || function == 121) - rseed = 14; - else if (function == 122 || function == 123 || function == 124) - rseed = 17; - else if (function == 125 || function == 126 || function == 127) - rseed = 19; - else if (function == 128 || function == 129 || function == 130) - rseed = 21; - else - rseed = (long) function; - - rrseed = rseed + (long) (10000 * instance); - bbob2009_gauss(&gval, 1, rrseed); - bbob2009_gauss(&gval2, 1, rrseed + 1); - return bbob2009_fmin(1000., bbob2009_fmax(-1000., bbob2009_round(100. * 100. * gval / gval2) / 100.)); -} diff --git a/src/problems/coco/suits/bbob/util_2009.rs b/src/problems/coco/suits/bbob/util_2009.rs deleted file mode 100644 index 639d50cb..00000000 --- a/src/problems/coco/suits/bbob/util_2009.rs +++ /dev/null @@ -1,42 +0,0 @@ -//! BBOB legacy code from 2009 -//! -//! This reuses the C legacy code to guarantee the same behaviour -//! as the official coco benchmark suite. - -extern "C" { - unsafe fn bbob2009_compute_xopt(xopt: *mut f64, seed: i64, dim: isize); - unsafe fn bbob2009_compute_fopt(function: isize, instance: isize) -> f64; -} - -pub fn compute_xopt(seed: usize, dimension: usize) -> Vec { - let mut xopt = vec![0.0; dimension]; - - unsafe { - bbob2009_compute_xopt(xopt.as_mut_ptr(), seed as i64, dimension as isize); - } - - xopt -} - -pub fn compute_fopt(function: usize, instance: usize) -> f64 { - unsafe { bbob2009_compute_fopt(function as isize, instance as isize) } -} - -#[cfg(test)] -mod tests { - use float_eq::assert_float_eq; - - #[test] - fn compute_xopt() { - let rseed = 4 + 10000 * 2; - let x = super::compute_xopt(rseed, 5); - let xe = vec![-3.123200, -1.584800, -3.537600, 1.694400, 3.956000]; - assert_float_eq!(&x, &xe, abs_all <= 0.0001); - } - - #[test] - fn compute_fopt() { - let y = super::compute_fopt(4, 2); - assert_float_eq!(y, 77.66, abs <= 0.0); - } -} diff --git a/src/problems/coco/suits/mod.rs b/src/problems/coco/suits/mod.rs deleted file mode 100644 index 0943be06..00000000 --- a/src/problems/coco/suits/mod.rs +++ /dev/null @@ -1,182 +0,0 @@ -use std::{ - fs, - io::Write, - path::PathBuf, - sync::{mpsc, Arc}, - thread, -}; - -use anyhow::Context; - -use crate::{ - framework::{self, legacy::Configuration}, - problems::coco::CocoInstance, - random::Random, - threads::SyncThreadPool, - tracking::{ - runtime_analysis::Experiment, - trigger::{EvalTrigger, IterTrigger}, - Log, - }, -}; - -mod toy; -pub use toy::new as toy; - -mod bbob; -pub use bbob::new as bbob; - -pub type SuiteGenerator = fn(function: usize, instance: usize, dimension: usize) -> CocoInstance; - -pub struct Suite { - functions: Vec, - next_function: usize, - instances: Vec, - next_instance: usize, - dimensions: Vec, - next_dimension: usize, - generator: SuiteGenerator, -} - -impl Suite { - pub fn new( - functions: Vec, - instances: Vec, - dimensions: Vec, - generator: SuiteGenerator, - ) -> Self { - Suite { - functions, - next_function: 0, - instances, - next_instance: 0, - dimensions, - next_dimension: 0, - generator, - } - } - - pub fn functions(&self) -> &[usize] { - &self.functions - } - - pub fn instances(&self) -> &[usize] { - &self.instances - } - - pub fn dimensions(&self) -> &[usize] { - &self.dimensions - } - - pub fn total_instances(&self) -> usize { - self.functions.len() * self.instances.len() * self.dimensions.len() - } - - fn current_instance(&self) -> CocoInstance { - (self.generator)( - self.functions[self.next_function], - self.instances[self.next_instance], - self.dimensions[self.next_dimension], - ) - } - - pub fn next_instance(&mut self) -> Option { - if self.next_function == self.functions.len() - || self.next_instance == self.instances.len() - || self.next_dimension == self.dimensions.len() - { - return None; - } - - let instance = self.current_instance(); - - self.next_instance += 1; - if self.next_instance == self.instances.len() { - self.next_instance = 0; - self.next_function += 1; - } - if self.next_function == self.functions.len() { - self.next_function = 0; - self.next_dimension += 1; - } - - Some(instance) - } -} - -impl Iterator for Suite { - type Item = CocoInstance; - - fn next(&mut self) -> Option { - self.next_instance() - } -} - -pub fn evaluate_suite( - suite: Suite, - configuration: Configuration, - output_dir: &str, -) -> anyhow::Result<()> { - let data_dir = Arc::new(PathBuf::from(output_dir)); - fs::create_dir_all(data_dir.as_ref())?; - - let runs = 1; - let total_runs = suite.total_instances() * (runs as usize); - let (tx, rx) = mpsc::channel(); - - let eval_trigger = EvalTrigger { - improvement: true, - interval: None, - }; - let iter_trigger = IterTrigger { - improvement: false, - interval: Some(10), - }; - - let configuration = Arc::new(configuration); - thread::spawn(move || { - let mut pool = SyncThreadPool::default(); - for problem in suite { - let tx = tx.clone(); - let data_dir = data_dir.clone(); - let configuration = configuration.clone(); - pool.enqueue(move || { - let result: anyhow::Result<()> = (|| { - let logger = &mut Log::new(eval_trigger, iter_trigger); - - let experiment_desc = problem.format_name(); - let data_dir = data_dir.join(experiment_desc); - - let random = Random::default(); - let experiment = - &mut Experiment::create(data_dir, &problem, &random, &configuration) - .context("creating experiment")?; - - for _ in 0..runs { - framework::legacy::run(&problem, logger, &configuration, None, None); - experiment.log_run(logger)?; - logger.clear(); - let _ = tx.send(Ok(())); - } - - Ok(()) - })(); - - if result.is_err() { - let _ = tx.send(result); - } - }); - } - }); - - let mut finished_runs = 0; - while finished_runs < total_runs { - rx.recv().unwrap()?; - finished_runs += 1; - print!("Runs: {}/{}\r", finished_runs, total_runs); - std::io::stdout().flush().unwrap(); - } - println!("\nDone."); - - Ok(()) -} diff --git a/src/problems/coco/suits/toy.rs b/src/problems/coco/suits/toy.rs deleted file mode 100644 index cf5b1945..00000000 --- a/src/problems/coco/suits/toy.rs +++ /dev/null @@ -1,43 +0,0 @@ -use crate::problems::coco::{functions, suits::Suite, CocoInstance, FunctionObject, Problem}; - -pub fn new() -> Suite { - Suite::new( - vec![1, 2, 3, 4, 5, 6], - vec![1], - vec![2, 3, 5, 10, 20, 40], - generator, - ) -} - -fn toy_problem(function: FunctionObject) -> Problem { - Problem { - input_transformations: vec![], - function, - output_transformations: vec![], - } -} - -fn generator(function: usize, instance: usize, dimension: usize) -> CocoInstance { - assert_eq!(instance, 1, "Toy suite only contains one instance"); - - let problem = match function { - 1 => toy_problem(functions::Sphere.into()), - 2 => toy_problem(functions::Ellipsoid.into()), - 3 => toy_problem(functions::Rastrigin.into()), - 4 => toy_problem(functions::BuecheRastrigin.into()), - 5 => toy_problem(functions::LinearSlope.into()), - 6 => toy_problem(functions::Rosenbrock.into()), - _ => panic!( - "Toy suite only contains 6 functions ({} was requested)", - function - ), - }; - - CocoInstance { - suite: "Toy", - problem, - function, - instance, - dimension, - } -} diff --git a/src/problems/coco/transformations.rs b/src/problems/coco/transformations.rs deleted file mode 100644 index 303a8b84..00000000 --- a/src/problems/coco/transformations.rs +++ /dev/null @@ -1,204 +0,0 @@ -#![allow(unused_variables)] - -pub mod input { - use crate::problems::coco::InputTransformation; - - pub struct Permutation { - pub mapping: Vec, - } - impl Permutation { - pub fn new(mapping: Vec) -> Box { - Box::new(Permutation { mapping }) - } - } - impl InputTransformation for Permutation { - fn apply(&self, x: &[f64], out: &mut [f64]) { - debug_assert_eq!(x.len(), self.mapping.len()); - for (o, m) in out.iter_mut().zip(self.mapping.iter()) { - *o = x[*m]; - } - } - - fn reverse(&self, x: &[f64], out: &mut [f64]) { - debug_assert_eq!(x.len(), self.mapping.len()); - for (o, m) in x.iter().zip(self.mapping.iter()) { - out[*m] = *o; - } - } - } - - pub struct Translate { - pub translation: Vec, - } - impl Translate { - pub fn new(translation: Vec) -> Box { - Box::new(Translate { translation }) - } - } - impl InputTransformation for Translate { - fn apply(&self, x: &[f64], out: &mut [f64]) { - for (i, xi) in x.iter().enumerate() { - out[i] = xi - self.translation[i]; - } - } - - fn reverse(&self, x: &[f64], out: &mut [f64]) { - for (i, xi) in x.iter().enumerate() { - out[i] = xi + self.translation[i]; - } - } - } - - pub struct Scale { - pub scales: Vec, - } - impl Scale { - pub fn new(scales: Vec) -> Box { - Box::new(Scale { scales }) - } - } - impl InputTransformation for Scale { - fn apply(&self, x: &[f64], out: &mut [f64]) { - for (i, xi) in x.iter().enumerate() { - out[i] = xi * self.scales[i]; - } - } - - fn reverse(&self, x: &[f64], out: &mut [f64]) { - for (i, xi) in x.iter().enumerate() { - out[i] = xi / self.scales[i]; - } - } - } - - pub struct Oscillate; - impl Oscillate { - pub fn new() -> Box { - Box::new(Oscillate) - } - } - impl InputTransformation for Oscillate { - fn apply(&self, x: &[f64], oscillated_x: &mut [f64]) { - let alpha = 0.1; - - for i in 0..x.len() { - if x[i] > 0.0 { - let tmp = f64::ln(x[i]) / alpha; - let base = f64::exp(tmp + 0.49 * (f64::sin(tmp) + f64::sin(0.79 * tmp))); - oscillated_x[i] = f64::powf(base, alpha); - } else if x[i] < 0.0 { - let tmp = f64::ln(-x[i]) / alpha; - let base = f64::exp(tmp + 0.49 * (f64::sin(0.55 * tmp) + f64::sin(0.31 * tmp))); - oscillated_x[i] = -f64::powf(base, alpha); - } else { - oscillated_x[i] = 0.0; - } - } - } - - fn reverse(&self, x: &[f64], out: &mut [f64]) { - todo!() - } - } - - pub struct Condition { - pub alpha: f64, - } - impl Condition { - pub fn new(alpha: f64) -> Box { - Box::new(Condition { alpha }) - } - } - impl InputTransformation for Condition { - fn apply(&self, x: &[f64], out: &mut [f64]) { - for i in 0..x.len() { - let scale = (i as f64) / (x.len() as f64 - 1.0); - out[i] = f64::powf(self.alpha, 0.5 * self.alpha * scale) * x[i]; - } - } - - fn reverse(&self, x: &[f64], out: &mut [f64]) { - todo!() - } - } - - pub struct Asymmetric { - pub beta: f64, - } - impl Asymmetric { - pub fn new(beta: f64) -> Box { - Box::new(Asymmetric { beta }) - } - } - impl InputTransformation for Asymmetric { - fn apply(&self, x: &[f64], out: &mut [f64]) { - for i in 0..x.len() { - if x[i] > 0.0 { - let scale = (i as f64) / (x.len() as f64 - 1.0); - let exponent = 1.0 + self.beta * scale * f64::sqrt(x[i]); - out[i] = f64::powf(x[i], exponent); - } else { - out[i] = x[i]; - } - } - } - - fn reverse(&self, x: &[f64], out: &mut [f64]) { - todo!() - } - } - - /// Implementation of the ominous 's_i scaling' of the BBOB Bueche-Rastrigin problem. - pub struct Brs; - impl Brs { - pub fn new() -> Box { - Box::new(Brs) - } - } - impl InputTransformation for Brs { - fn apply(&self, x: &[f64], out: &mut [f64]) { - for i in 0..x.len() { - let scale = (i as f64) / (x.len() as f64 - 1.0); - /* Function documentation says we should compute 10^(0.5 * - * (i-1)/(D-1)). Instead we compute the equivalent - * sqrt(10)^((i-1)/(D-1)) just like the legacy code. - */ - let mut factor = f64::powf(f64::sqrt(10.0), scale); - /* Documentation specifies odd indices and starts indexing - * from 1, we use all even indices since C starts indexing - * with 0. - */ - if x[i] > 0.0 && i % 2 == 0 { - factor *= 10.0; - } - out[i] = factor * x[i]; - } - } - - fn reverse(&self, x: &[f64], out: &mut [f64]) { - todo!() - } - } -} - -pub mod output { - use crate::problems::coco::OutputTransformation; - - pub struct Translate { - pub translation: f64, - } - impl Translate { - pub fn new(translation: f64) -> Box { - Box::new(Translate { translation }) - } - } - impl OutputTransformation for Translate { - fn apply(&self, y: f64) -> f64 { - y + self.translation - } - - fn reverse(&self, y: f64) -> f64 { - y - self.translation - } - } -} diff --git a/src/problems/coco_bound/mod.rs b/src/problems/coco_bound/mod.rs deleted file mode 100644 index c84e3b6f..00000000 --- a/src/problems/coco_bound/mod.rs +++ /dev/null @@ -1,105 +0,0 @@ -use std::ops::RangeInclusive; - -use crate::{ - framework::{Individual, SingleObjective}, - problems::{self, Evaluator}, - state::{common::EvaluatorInstance, State}, -}; - -pub use coco_rs::{Problem, Suite}; - -pub mod suits; - -#[derive(serde::Serialize)] -pub struct CocoInstance { - function_idx: usize, - instance_idx: usize, - dimension_idx: usize, - - name: String, - dimension: usize, - ranges_of_interest: Vec>, - final_target_value: f64, -} - -impl CocoInstance { - pub fn format_name(&self) -> String { - self.name.clone() - } - - fn from(problem: &Problem) -> Self { - let name = problem.id().to_string(); - let dimension = problem.dimension(); - let ranges_of_interest = problem.get_ranges_of_interest(); - let final_target_value = problem.final_target_value(); - - CocoInstance { - function_idx: problem.function_index(), - instance_idx: problem.instance_index(), - dimension_idx: problem.dimension_index(), - - name, - dimension, - ranges_of_interest, - final_target_value, - } - } -} - -impl problems::Problem for CocoInstance { - type Encoding = Vec; - type Objective = SingleObjective; - - fn name(&self) -> &str { - "Coco" - } - - fn default_evaluator<'a>(&self) -> EvaluatorInstance<'a, Self> { - unimplemented!("the evaluator has to be inserted manually") - } -} - -impl problems::VectorProblem for CocoInstance { - type T = f64; - - fn dimension(&self) -> usize { - self.dimension - } -} - -impl problems::LimitedVectorProblem for CocoInstance { - fn range(&self, dimension: usize) -> std::ops::Range { - let range = self.ranges_of_interest[dimension].clone(); - - let (start, end) = range.into_inner(); - start..end - } -} - -impl problems::HasKnownTarget for CocoInstance { - fn target_hit(&self, target: SingleObjective) -> bool { - target.value() <= self.final_target_value - } -} - -struct CocoEvaluator<'s> { - pub problem: Problem<'s>, -} - -impl Evaluator for CocoEvaluator<'_> { - type Problem = CocoInstance; - - fn evaluate( - &mut self, - _problem: &Self::Problem, - _state: &mut State, - individuals: &mut [Individual], - ) { - for individual in individuals { - let mut out = [0.0]; - self.problem - .evaluate_function(individual.solution(), &mut out); - individual.evaluate(SingleObjective::try_from(out[0]).unwrap()) - } - } -} diff --git a/src/problems/coco_bound/suits.rs b/src/problems/coco_bound/suits.rs deleted file mode 100644 index b43c1dd1..00000000 --- a/src/problems/coco_bound/suits.rs +++ /dev/null @@ -1,136 +0,0 @@ -use crate::{ - framework::Configuration, - problems::{ - coco_bound::{CocoEvaluator, CocoInstance}, - HasKnownTarget, - }, - state::{common, State}, - tracking::{files, Log}, -}; -use anyhow::Context; -use coco_rs::{Suite, SuiteName}; -use std::{ - fs::{self, File}, - io::{BufWriter, Write}, - path::PathBuf, - sync::mpsc, - thread, -}; - -pub fn toy() -> Suite { - Suite::new(SuiteName::Toy, "", "").unwrap() -} - -pub fn bbob() -> Suite { - Suite::new(SuiteName::Bbob, "", "").unwrap() -} - -pub fn largescale() -> Suite { - Suite::new(SuiteName::BbobLargescale, "", "").unwrap() -} - -pub fn evaluate_suite( - mut suite: Suite, - configuration: Configuration, - output_dir: &str, - setup: impl Fn(&mut State) + Send + Sync, -) -> anyhow::Result<()> { - #[allow(unused_variables)] - let num_threads = 1; - let num_threads = num_cpus::get() as u32; - - let data_dir = &PathBuf::from(output_dir); - fs::create_dir_all(data_dir)?; - - let config_log_file = data_dir.join("configuration.ron"); - ron::ser::to_writer_pretty( - BufWriter::new( - File::create(config_log_file).context("failed to create configuration file")?, - ), - configuration.heuristic(), - ron::ser::PrettyConfig::default().struct_names(true), - ) - .context("failed to serialize configuration")?; - - let total_runs = suite.number_of_problems(); - let (tx, rx) = mpsc::channel(); - - coco_rs::set_log_level(coco_rs::LogLevel::Warning); - - let configuration = &configuration; - let setup = &setup; - - thread::scope(move |scope| { - scope.spawn(move || { - let mut pool = scoped_threadpool::Pool::new(num_threads); - - loop { - let (function_idx, dimension_idx, instance_idx) = match suite.next_problem(None) { - None => break, - Some(problem) => ( - problem.function_index(), - problem.dimension_index(), - problem.instance_index(), - ), - }; - - // Create a new suite, because COCO doesn't guarantee that - // multiple problems can be created from one suite simultaneously. - let mut suite = suite.clone(); - let tx = tx.clone(); - - pool.scoped(move |pool| { - pool.execute(move || { - let problem = suite - .problem_by_function_dimension_instance_index( - function_idx, - dimension_idx, - instance_idx, - ) - .unwrap(); - let instance = CocoInstance::from(&problem); - - let result: anyhow::Result<_> = (move || { - let experiment_desc = instance.format_name(); - let log_file = data_dir.join(format!("{}.log", experiment_desc)); - - let state = configuration.optimize_with(&instance, |state| { - state.insert(common::EvaluatorInstance::new(CocoEvaluator { - problem, - })); - setup(state); - }); - let log = state.get::(); - files::write_log_file(log_file, log)?; - - let target_hit = if let Some(fitness) = state.best_objective_value() { - instance.target_hit(*fitness) - } else { - false - }; - Ok(target_hit) - })(); - - let _ = tx.send(result); - }); - }); - } - }); - - let mut finished_runs = 0; - let mut successful_runs = 0; - while finished_runs < total_runs { - let hit = rx.recv().unwrap()?; - finished_runs += 1; - successful_runs += i32::from(hit); - print!( - "Runs: {}/{}/{}\r", - successful_runs, finished_runs, total_runs - ); - std::io::stdout().flush().unwrap(); - } - println!("\nDone."); - - Ok(()) - }) -} diff --git a/src/problems/encoding.rs b/src/problems/encoding.rs new file mode 100644 index 00000000..dc6c7de0 --- /dev/null +++ b/src/problems/encoding.rs @@ -0,0 +1,20 @@ +//! Solution encodings. + +use std::collections::btree_set::BTreeSet; + +use trait_set::trait_set; + +trait_set! { + /// Collection of traits required by every solution encoding. + pub trait AnyEncoding = Clone + PartialEq + Send; +} + +/// Validates whether the provided permutation is valid, i.e. if the elements are unique. +pub fn valid_permutation(iter: T) -> bool +where + T: IntoIterator, + T::Item: Eq + Ord, +{ + let mut uniq = BTreeSet::new(); + iter.into_iter().all(move |x| uniq.insert(x)) +} diff --git a/src/problems/evaluate.rs b/src/problems/evaluate.rs new file mode 100644 index 00000000..c6b7f04f --- /dev/null +++ b/src/problems/evaluate.rs @@ -0,0 +1,281 @@ +//! Evaluate [`Individual`]s according to some objective function. + +use std::marker::PhantomData; + +use better_any::{Tid, TidAble}; +use rayon::iter::{IntoParallelRefMutIterator, ParallelIterator}; +use trait_set::trait_set; + +use crate::{CustomState, Individual, Problem, State}; + +/// Trait for evaluating individuals, i.e. evaluate their solutions to an optimization problem. +/// +/// Implement [`ObjectiveFunction`] instead if the objective function is static, +/// i.e. does not depend on `&self` or `&mut self` to automatically implement this trait +/// and gain default implementations for sequential and parallel evaluation through +/// [`Sequential`] and [`Parallel`]. +/// +/// # Examples +/// +/// A simple implementation of the n-dimensional real-valued sphere function `f(x) = x^2`. +/// +/// Note that implementing [`ObjectiveFunction`] is preferred in this case, because the +/// objective function only depends on `x`. +/// +/// ``` +/// use mahf::{Individual, Problem, SingleObjective, State, problems::Evaluate}; +/// +/// pub struct Sphere { +/// pub dim: usize, +/// } +/// +/// impl Problem for Sphere { +/// type Encoding = Vec; +/// type Objective = SingleObjective; +/// +/// fn name(&self) -> &str { +/// "Sphere" +/// } +/// } +/// +/// #[derive(Default)] // No explicit implementation of `TryDefault` necessary through this `derive`. +/// pub struct SequentialSphereEvaluator; +/// +/// impl Evaluate for SequentialSphereEvaluator { +/// type Problem = Sphere; +/// +/// /// Implements `f(x) = \sum (x_i)^2`. +/// fn evaluate( +/// &mut self, +/// _problem: &Self::Problem, +/// _state: &mut State, +/// individuals: &mut [Individual]) +/// { +/// for individual in individuals { +/// individual.evaluate_with(|solution| { +/// solution +/// .iter() +/// .map(|x| x.powi(2)) +/// .sum::() +/// .try_into() +/// .unwrap() +/// }) +/// } +/// } +/// } +/// ``` +pub trait Evaluate: Send { + /// The type of optimization problem. + type Problem: Problem; + + /// Evaluates individuals on the [`Problem`]. + /// + /// [`Problem`]: Evaluate::Problem + fn evaluate( + &mut self, + problem: &Self::Problem, + state: &mut State, + individuals: &mut [Individual], + ); +} + +trait_set! { + /// Collection of traits to allow storing an evaluator, i.e. + /// a struct implementing [`Evaluate`], in the [`State`]. + pub trait Evaluator = Evaluate + for<'a> CustomState<'a> + for<'a> TidAble<'a>; +} + +/// Trait for a static objective function of an optimization problem. +/// +/// [`Sequential`] and [`Parallel`] provide a default implementation of sequential and parallel +/// evaluation using the [`objective`], respectively. +/// +/// If your objective function is not static, i.e. takes `&self` or `&mut self`, implement +/// [`Evaluate`] directly. +/// +/// [`objective`]: ObjectiveFunction::objective +/// +/// # Examples +/// +/// A simple implementation of the n-dimensional real-valued sphere function `f(x) = x^2`. +/// +/// ``` +/// use mahf::{Individual, Problem, SingleObjective, State, problems::ObjectiveFunction}; +/// +/// pub struct Sphere { +/// pub dim: usize, +/// } +/// +/// impl Problem for Sphere { +/// type Encoding = Vec; +/// type Objective = SingleObjective; +/// +/// fn name(&self) -> &str { +/// "Sphere" +/// } +/// } +/// +/// impl ObjectiveFunction for Sphere { +/// /// Implements `f(x) = \sum (x_i)^2`. +/// fn objective(solution: &Self::Encoding) -> Self::Objective { +/// solution +/// .iter() +/// .map(|x| x.powi(2)) +/// .sum::() +/// .try_into() +/// .unwrap() +/// } +/// } +/// ``` +pub trait ObjectiveFunction: Problem { + /// Calculates the objective value of the given `solution`. + fn objective(solution: &Self::Encoding) -> Self::Objective; +} + +/// A sequential evaluator for an optimization problem, i.e. [`ObjectiveFunction`]. +/// +/// The evaluator simply evaluates all individuals sequentially in order. +#[derive(Tid)] +pub struct Sequential(PhantomData P>); + +impl Sequential

{ + /// Creates a new instance of a sequential evaluator for a problem `P`. + /// + /// # Examples + /// + /// ``` + /// use mahf::{Individual, Problem, SingleObjective, State, problems::ObjectiveFunction}; + /// use mahf::problems::evaluate::Sequential; + /// + /// pub struct Sphere { + /// pub dim: usize, + /// } + /// + /// impl Problem for Sphere { + /// type Encoding = Vec; + /// type Objective = SingleObjective; + /// + /// fn name(&self) -> &str { + /// "Sphere" + /// } + /// } + /// + /// impl ObjectiveFunction for Sphere { + /// /// Implements `f(x) = \sum (x_i)^2`. + /// fn objective(solution: &Self::Encoding) -> Self::Objective { + /// solution + /// .iter() + /// .map(|x| x.powi(2)) + /// .sum::() + /// .try_into() + /// .unwrap() + /// } + /// } + /// + /// // Create a sequential evaluator for `Sphere` + /// let sequential_evaluator = Sequential::::new(); + /// ``` + pub fn new() -> Self { + Self(PhantomData) + } +} + +impl Default for Sequential

{ + fn default() -> Self { + Self::new() + } +} + +impl

Evaluate for Sequential

+where + P: ObjectiveFunction, +{ + type Problem = P; + + fn evaluate( + &mut self, + _problem: &Self::Problem, + _state: &mut State, + individuals: &mut [Individual], + ) { + for individual in individuals { + individual.evaluate_with(P::objective); + } + } +} + +impl CustomState<'_> for Sequential

{} + +/// A parallel evaluator for an optimization problem. +/// +/// The evaluator evaluates the individuals in parallel using the [`rayon`] library. +#[derive(Tid)] +pub struct Parallel(PhantomData P>); + +impl Parallel

{ + /// Creates a new instance of a parallel evaluator for a problem `P`. + /// + /// # Examples + /// + /// ``` + /// use mahf::{Individual, Problem, SingleObjective, State, problems::ObjectiveFunction}; + /// use mahf::problems::evaluate::Parallel; + /// + /// pub struct Sphere { + /// pub dim: usize, + /// } + /// + /// impl Problem for Sphere { + /// type Encoding = Vec; + /// type Objective = SingleObjective; + /// + /// fn name(&self) -> &str { + /// "Sphere" + /// } + /// } + /// + /// impl ObjectiveFunction for Sphere { + /// /// Implements `f(x) = \sum (x_i)^2`. + /// fn objective(solution: &Self::Encoding) -> Self::Objective { + /// solution + /// .iter() + /// .map(|x| x.powi(2)) + /// .sum::() + /// .try_into() + /// .unwrap() + /// } + /// } + /// + /// // Create a parallel evaluator for `Sphere` + /// let parallel_evaluator = Parallel::::new(); + /// ``` + pub fn new() -> Self { + Self(PhantomData) + } +} + +impl Default for Parallel

{ + fn default() -> Self { + Self::new() + } +} + +impl

Evaluate for Parallel

+where + P: ObjectiveFunction + Problem, +{ + type Problem = P; + + fn evaluate( + &mut self, + _problem: &Self::Problem, + _state: &mut State, + individuals: &mut [Individual], + ) { + individuals + .par_iter_mut() + .for_each(|individual| individual.evaluate_with(P::objective)); + } +} + +impl CustomState<'_> for Parallel

{} diff --git a/src/problems/individual.rs b/src/problems/individual.rs new file mode 100644 index 00000000..be36c163 --- /dev/null +++ b/src/problems/individual.rs @@ -0,0 +1,289 @@ +//! Encoded solution to an optimization problem with an associated +//! (optional) objective value. + +use std::fmt::{Debug, Formatter}; + +use crate::problems::Problem; + +/// An encoded solution with an associated (optional) objective value. +/// +/// The objective value is automatically reset when mutating the underlying solution. +pub struct Individual { + solution: P::Encoding, + objective: Option, +} + +impl Individual

{ + /// Constructs a new individual from a given solution and objective value. + /// + /// # Examples + /// + /// Creating `n` individuals with some default solution and infinite objective value: + /// + /// ``` + /// use mahf::{Individual, SingleObjectiveProblem}; + /// + /// pub fn create_n_default_individuals

(n: usize) -> Vec> + /// where + /// P: SingleObjectiveProblem, + /// P::Encoding: Default, + /// { + /// (0..n) + /// .map(|_| Individual::new(P::Encoding::default(), f64::INFINITY.try_into().unwrap())) + /// .collect() + /// } + /// ``` + pub fn new(solution: P::Encoding, objective: P::Objective) -> Self { + Self { + solution, + objective: Some(objective), + } + } + + /// Constructs a new `Individual` from a solution, leaving it unevaluated. + /// + /// Note that the [`IntoIndividuals`] trait allows this for collections of solutions. + /// + /// [`IntoIndividuals`]: crate::population::IntoIndividuals + /// + /// # Examples + /// + /// Creating `n` individuals with some default solution and no objective value. + /// + /// Note that [`Individual::default`] could be used instead. + /// + /// ``` + /// use mahf::{Individual, Problem}; + /// + /// pub fn create_n_default_individuals

(n: usize) -> Vec> + /// where + /// P: Problem, + /// P::Encoding: Default, + /// { + /// (0..n) + /// .map(|_| Individual::new_unevaluated(P::Encoding::default())) + /// .collect() + /// } + pub fn new_unevaluated(solution: P::Encoding) -> Self { + Self { + solution, + objective: None, + } + } + + /// Evaluates the the solution with some objective function. + /// + /// This method is usually only called in [`Evaluate`] implementations. + /// + /// [`Evaluate`]: crate::problems::Evaluate + pub fn evaluate_with(&mut self, objective_fn: F) + where + F: Fn(&P::Encoding) -> P::Objective, + { + self.objective = Some(objective_fn(&self.solution)); + } + + /// Sets the objective value directly, returning if an existing value was overwritten. + /// + /// This method is usually only called in [`Evaluate`] implementations. + /// + /// [`Evaluate`]: crate::problems::Evaluate + pub fn set_objective(&mut self, objective: P::Objective) -> bool { + let evaluated = self.objective.is_some(); + self.objective = Some(objective); + evaluated + } + + /// Returns a reference to the solution. + /// + /// # Examples + /// + /// ``` + /// use mahf::Individual; + /// use mahf::problems::VectorProblem; + /// + /// pub fn example

(problem: &P) where P: VectorProblem { + /// // The default solution for `VectorProblem` is all zeros. + /// assert_eq!(Individual::

::default().solution(), &vec![0.0; problem.dimension()]) + /// } + /// ``` + pub fn solution(&self) -> &P::Encoding { + &self.solution + } + + /// Returns the mutable reference to the solution, resetting the objective value. + /// + /// + /// # Examples + /// + /// ``` + /// use mahf::{Individual, SingleObjective}; + /// use mahf::problems::VectorProblem; + /// + /// pub fn example

(problem: &P) where P: VectorProblem { + /// let mut individual = Individual::

::new(P::Encoding::default(), f64::INFINITY.try_into().unwrap()); + /// assert!(individual.is_evaluated()); + /// // Overwrite the solution with all ones. + /// *individual.solution_mut() = vec![1.0; problem.dimension()]; + /// // The objective value was reset. + /// assert!(!individual.is_evaluated()); + /// } + /// ``` + pub fn solution_mut(&mut self) -> &mut P::Encoding { + self.objective = None; + &mut self.solution + } + + /// Returns the solution, consuming the individual in the process. + /// + /// # Examples + /// + /// ``` + /// use mahf::Individual; + /// use mahf::problems::VectorProblem; + /// + /// pub fn example

(problem: &P) where P: VectorProblem { + /// // The default solution for `VectorProblem` is all zeros. + /// assert_eq!(Individual::

::default().into_solution(), vec![0.0; problem.dimension()]) + /// } + /// ``` + pub fn into_solution(self) -> P::Encoding { + self.solution + } + + /// Returns if the `Individual` contains an objective value. + /// + /// # Examples + /// + /// ``` + /// use mahf::{Individual, SingleObjective}; + /// use mahf::problems::VectorProblem; + /// + /// pub fn example

(problem: &P) where P: VectorProblem { + /// // Explicitly assign Inf as objective value. + /// let individual = Individual::

::new(P::Encoding::default(), f64::INFINITY.try_into().unwrap()); + /// assert!(individual.is_evaluated()); + /// // `Individual::default` constructs the individual without objective value. + /// let individual = Individual::

::default(); + /// assert!(!individual.is_evaluated()); + /// } + /// ``` + pub fn is_evaluated(&self) -> bool { + self.objective.is_some() + } + + /// Returns the objective value. + /// + /// # Examples + /// + /// ``` + /// use mahf::{Individual, SingleObjective}; + /// use mahf::problems::VectorProblem; + /// + /// pub fn example

(problem: &P) where P: VectorProblem { + /// // Explicitly assign Inf as objective value. + /// let individual = Individual::

::new(P::Encoding::default(), f64::INFINITY.try_into().unwrap()); + /// assert_eq!(individual.get_objective(), Some(&f64::INFINITY.try_into().unwrap())); + /// // `Individual::default` constructs the individual without objective value. + /// let individual = Individual::

::default(); + /// assert_eq!(individual.get_objective(), None); + /// } + /// ``` + pub fn get_objective(&self) -> Option<&P::Objective> { + self.objective.as_ref() + } + + /// Returns the objective value. + /// + /// # Panics + /// + /// Panics if the individual is not evaluated. + /// + /// Use [`is_evaluated`] to check for evaluation beforehand or use [`get_objective`]. + /// + /// [`is_evaluated`]: Individual::is_evaluated + /// [`get_objective`]: Individual::get_objective + /// + /// # Examples + /// + /// ``` + /// use mahf::{Individual, SingleObjective}; + /// use mahf::problems::VectorProblem; + /// + /// pub fn example

(problem: &P) where P: VectorProblem { + /// // Explicitly assign Inf as objective value. + /// let individual = Individual::

::new(P::Encoding::default(), f64::INFINITY.try_into().unwrap()); + /// assert_eq!(individual.objective(), &f64::INFINITY.try_into().unwrap()); + /// // `Individual::default` constructs the individual without objective value. + /// let individual = Individual::

::default(); + /// // `individual.objective()` panics. + /// } + /// ``` + pub fn objective(&self) -> &P::Objective { + self.objective.as_ref().unwrap() + } +} + +impl

Individual

+where + P: Problem, + P::Encoding: Default, +{ + /// Construct a pseudo individual using the default solution. + /// + /// Should only be used for testing. + pub(crate) fn new_test_unit(objective: P::Objective) -> Self { + Self::new(P::Encoding::default(), objective) + } +} + +impl

Default for Individual

+where + P: Problem, + P::Encoding: Default, +{ + /// Constructs a new individual using the default solution and no objective value. + /// + /// # Examples + /// + /// ``` + /// use mahf::Individual; + /// use mahf::problems::VectorProblem; + /// + /// pub fn example

(problem: &P) where P: VectorProblem { + /// assert_eq!(Individual::

::default(), Individual::new_unevaluated(P::Encoding::default())); + /// } + /// ``` + fn default() -> Self { + Self::new_unevaluated(P::Encoding::default()) + } +} + +impl Clone for Individual

{ + fn clone(&self) -> Self { + Self { + solution: self.solution.clone(), + objective: self.objective.clone(), + } + } +} + +impl PartialEq for Individual

{ + fn eq(&self, other: &Self) -> bool { + self.solution == other.solution && self.objective == other.objective + } +} + +impl> Debug for Individual

{ + fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result { + let mut debug = f.debug_struct("Individual"); + + debug.field("solution", &self.solution); + + if let Some(objective) = self.get_objective() { + debug.field("objective", objective); + } + + debug.finish() + } +} diff --git a/src/problems/mod.rs b/src/problems/mod.rs index 8c44cf47..5a58f809 100644 --- a/src/problems/mod.rs +++ b/src/problems/mod.rs @@ -1,100 +1,382 @@ -//! Collection of common test problems. +//! Optimization problems. //! -//! Every problem implements the [Problem] trait. +//! The `problems` module provides traits and structures for defining and working with +//! optimization problems. An optimization problem involves finding the best solution +//! from a set of possible solutions, where the quality of a solution is determined by +//! an objective function. //! -//! If a given problem has certain properties, then those will be expressed as traits as well. -//! Those traits are quite specific and when writing a component they'll allow you to only require those you really need. +//! ## Minimization +//! +//! In MAHF, "optimizing" is currently equivalent to "minimizing", i.e. finding the solution +//! that minimizes some objective function. +//! +//! # Key Concepts +//! +//! - `Problem`: The [`Problem`] trait (hierarchy) provides information about the problem +//! to components. Traits built upon [`Problem`] (e.g. [`VectorProblem`]) allow making +//! *any* information accessible to components, while being as generic as possible. +//! - `Individual`: An [`Individual`] is an encoded solution to the problem along with an associated +//! (optional) objective value, which qualifies how "good" of a solution it is to the problem. +//! In single-objective optimization, this objective value is also referred to as "fitness". +//! - `Evaluate`: The [`Evaluate`] trait allows evaluating [`Individual`]s according to some +//! objective function. +//! +//! # Usage +//! +//! ## Pre-implemented problems +//! There exist several pre-implemented problems in the MAHF ecosystem, which provide a good +//! starting point. +//! +//! TODO: Reference mahf-problems repo(s) here. +//! +//! ## Implement custom problems +//! +//! To define your own optimization problem, the minimum requirement is to implement +//! [`Problem`] and some sort of [`Evaluator`] for it, i.e. implement [`Evaluate`] for +//! some struct. +//! +//! Note that for most problems with a *static* objective function, +//! the [`ObjectiveFunction`] trait should be preferred over [`Evaluate`]. +//! See [`ObjectiveFunction`] for more information. +//! +//! Then implement the traits built on top of [`Problem`] found in this module where sensible, +//! e.g. [`VectorProblem`] for an optimization problem with a vector-based solution encoding. +//! You can similarly define own traits based on [`Problem`] to allow your custom components +//! to access any problem-specific information. + +use std::ops::Range; -use crate::{ - framework::{Individual, MultiObjective, Objective, SingleObjective}, - state::{common::EvaluatorInstance, State}, -}; -use std::{any::Any, ops::Range}; +use trait_set::trait_set; -pub mod bmf; -pub mod coco_bound; -pub mod tsp; +pub mod encoding; +pub mod evaluate; +pub mod individual; +pub mod objective; -#[cfg(never)] -pub mod coco; +use crate::state::common::EvaluatorInstance; +pub use encoding::AnyEncoding; +pub use evaluate::{Evaluate, Evaluator, ObjectiveFunction}; +pub use individual::Individual; +pub use objective::{MultiObjective, Objective, SingleObjective}; -/// Base trait for all problems. +/// An optimization (minimization) problem. /// -/// Defines the problems encoding and objective. +/// This trait is the base trait for all problems, and itself only defines +/// - a encoding to solutions to the problem ([`Problem::Encoding`]), +/// - the type of objective to minimize ([`Problem::Objective`]), and +/// - the name of the problem ([`Problem::name`]). +/// +/// # Problem-specific information +/// +/// `Problem` (along with traits that build upon it, e.g. [`VectorProblem`]) make problem-specific +/// information accessible to components, and should only provide exactly as much information +/// as the components need to function. +/// +/// TODO: Reference component (module) documentation here. +/// +/// # Examples +/// +/// A simple implementation of the real-valued sphere function `f(x) = x^2`: +/// +/// ``` +/// use mahf::{Problem, SingleObjective}; +/// +/// pub struct Sphere { +/// pub dim: usize, +/// } +/// +/// impl Problem for Sphere { +/// type Encoding = Vec; // real-valued vector +/// type Objective = SingleObjective; +/// +/// fn name(&self) -> &str { +/// "Sphere" +/// } +/// } +/// ``` pub trait Problem: 'static { - /// The datatype representing the problem. - type Encoding: Any + Clone + PartialEq + Send; + /// The encoding of a solution to the optimization problem (genotype). + type Encoding: AnyEncoding; - /// The objective. + /// The objective type to minimize. /// - /// See [SingleObjective] and [MultiObjective]. + /// See [`SingleObjective`] and [`MultiObjective`] for the default options. type Objective: Objective; - /// The name of the problem. + /// The name of the optimization problem. fn name(&self) -> &str; /// Returns the default evaluator for the problem. /// - /// Can be set to [unimplemented] when the evaluator - /// requires additional setup by the user. - fn default_evaluator<'a>(&self) -> EvaluatorInstance<'a, Self>; + /// To be removed in the future in favor of a [`TryDefault`] bound on [`Evaluate`]. + /// + /// [`TryDefault`]: crate::utils::TryDefault + #[deprecated] + fn default_evaluator<'a>(&self) -> EvaluatorInstance<'a, Self> { + unimplemented!() + } } -/// Defines how a population should be evaluated. -pub trait Evaluator: Send { - type Problem: Problem; - - /// Evaluates all individuals. +trait_set! { + /// An optimization problem with a single objective or multiple combined objectives. /// - /// After calling this function, [Individual::is_evaluated] - /// should be true for all individuals. + /// This trait should be used in favor over specifying the objective type + /// of [`Problem`] directly, and is automatically implemented for all problems + /// with [`SingleObjective`] as [`Problem::Objective`]. + pub trait SingleObjectiveProblem = Problem; + + /// An optimization problem with multiple distinct objectives. /// - /// Individuals might already be evaluated prior to calling this function, - /// in which case they can be skipped. - fn evaluate( - &mut self, - problem: &Self::Problem, - state: &mut State, - individuals: &mut [Individual], - ); + /// This trait should be used in favor over specifying the objective type + /// of [`Problem`] directly, and is automatically implemented for all problems + /// with [`MultiObjective`] as [`Problem::Objective`]. + pub trait MultiObjectiveProblem = Problem; } -/// A single objective problem. -pub trait SingleObjectiveProblem: Problem {} - -impl> SingleObjectiveProblem for P {} - -/// A multi objective problem. -pub trait MultiObjectiveProblem: Problem {} - -impl> MultiObjectiveProblem for P {} - -/// A problem with fixed length array like encoding. -pub trait VectorProblem: Problem { - /// Type of the vectors elements. - type T: Any + Clone; +/// A vector-based optimization problem. +/// +/// This trait extends the [`Problem`] trait and represents an optimization problem +/// whose solutions are encoded as vectors with some [`Element`] type. +/// +/// [`Element`]: VectorProblem::Element +/// +/// # Examples +/// +/// A simple implementation of the n-dimensional real-valued sphere function `f(x) = x^2`: +/// +/// ``` +/// use mahf::{Problem, SingleObjective, problems::VectorProblem}; +/// +/// pub struct Sphere { +/// pub dim: usize, +/// } +/// +/// impl Problem for Sphere { +/// type Encoding = Vec; +/// type Objective = SingleObjective; +/// +/// fn name(&self) -> &str { +/// "Sphere" +/// } +/// } +/// +/// impl VectorProblem for Sphere { +/// type Element = f64; +/// +/// fn dimension(&self) -> usize { +/// self.dim +/// } +/// } +/// ``` +pub trait VectorProblem: Problem> { + /// The element type of the vector encoding the solutions. + type Element: Clone; - /// Returns the dimension of the vector. + /// Returns the dimension of the optimization problem. + /// + /// The dimension represents the length of the vector encoding the solutions. fn dimension(&self) -> usize; } -/// A [VectorProblem] where each dimension has a limited range. +/// A vector-based optimization problem with limited search space. +/// +/// This trait extends the [`VectorProblem`] trait and represents an optimization problem +/// whose solutions are encoded as vectors with a limited search space defined by some domain. +/// +/// # Examples +/// +/// A simple implementation of the n-dimensional real-valued sphere function `f(x) = x^2` +/// restricted to the \[-1, 1) domain on each dimension: +/// +/// ``` +/// use std::ops::{Range, RangeInclusive}; +/// use mahf::{Problem, SingleObjective, problems::{VectorProblem, LimitedVectorProblem}}; +/// +/// pub struct Sphere { +/// pub dim: usize, +/// } +/// +/// impl Problem for Sphere { +/// type Encoding = Vec; +/// type Objective = SingleObjective; +/// +/// fn name(&self) -> &str { +/// "Sphere" +/// } +/// } +/// +/// impl VectorProblem for Sphere { +/// type Element = f64; +/// +/// fn dimension(&self) -> usize { +/// self.dim +/// } +/// } +/// +/// impl LimitedVectorProblem for Sphere { +/// fn domain(&self) -> Vec> { +/// std::iter::repeat(-1.0..1.0).take(self.dim).collect() +/// } +/// } +/// ``` pub trait LimitedVectorProblem: VectorProblem { - /// Returns the range of the given dimension. - fn range(&self, dimension: usize) -> Range; + /// Returns the bounds of the search space for each element in the vector encoding. + /// + /// The bounds specify the range of valid values for each element in the vector. + fn domain(&self) -> Vec>; } -/// A [Problem] where one can check for the target. -pub trait HasKnownTarget { - /// Returns whether the target has been reached. - fn target_hit(&self, target: SingleObjective) -> bool; +/// A single-objective optimization problem with an indicator for reaching the optimum. +/// +/// If the objective value of the optimum is known directly, implement [`KnownOptimumProblem`] +/// instead, which provides a blanket implementation for this trait. +/// +/// # Examples +/// +/// A simple implementation of the real-valued sphere function `f(x) = x^2`, where +/// the optimum is known to be 0. +/// +/// Note that a implementation of [`KnownOptimumProblem`] is more sensible in this case because +/// the value is known directly. +/// +/// ``` +/// use std::ops::Range; +/// use mahf::{Problem, SingleObjective, problems::OptimumReachedProblem}; +/// +/// pub struct Sphere { +/// pub dim: usize, +/// } +/// +/// impl Problem for Sphere { +/// type Encoding = Vec; +/// type Objective = SingleObjective; +/// +/// fn name(&self) -> &str { +/// "Sphere" +/// } +/// } +/// +/// impl OptimumReachedProblem for Sphere { +/// fn optimum_reached(&self, objective: SingleObjective) -> bool { +/// // Approximately 0 is accepted as optimum. +/// objective.value() < 1e-8 +/// } +/// } +/// +/// // A value smaller as 1e-8 counts as optimum. +/// let sphere = Sphere { dim: 1 }; +/// assert!(sphere.optimum_reached(1e-9.try_into().unwrap())); +/// ``` +pub trait OptimumReachedProblem: SingleObjectiveProblem { + /// Checks whether the objective value has reached the optimum. + fn optimum_reached(&self, objective: SingleObjective) -> bool; } -/// A [Problem] with known target value. +/// A single-objective optimization problem with a known optimum value. +/// +/// # Examples +/// +/// A simple implementation of the real-valued sphere function `f(x) = x^2`, where +/// the optimum is known to be 0. +/// +/// ``` +/// use std::ops::Range; +/// use mahf::{Problem, SingleObjective, problems::{KnownOptimumProblem, OptimumReachedProblem}}; +/// +/// pub struct Sphere { +/// pub dim: usize, +/// } /// -/// This is a stricter requirement than [HasKnownTarget]. -/// - When writing a component, prefer [HasKnownTarget] when possible. -/// When implementing this for a problem, always implement [HasKnownTarget] as well. -pub trait HasKnownOptimum { +/// impl Problem for Sphere { +/// type Encoding = Vec; +/// type Objective = SingleObjective; +/// +/// fn name(&self) -> &str { +/// "Sphere" +/// } +/// } +/// +/// impl KnownOptimumProblem for Sphere { +/// const DELTA: f64 = 1e-10; +/// +/// fn known_optimum(&self) -> SingleObjective { +/// 0.0.try_into().unwrap() +/// } +/// } +/// +/// // A value greater than 1e-10 does not counts as optimum. +/// let sphere = Sphere { dim: 1 }; +/// assert!(!sphere.optimum_reached(1e-9.try_into().unwrap())); +/// ``` +pub trait KnownOptimumProblem: SingleObjectiveProblem { + /// A constant representing the tolerance level for comparing objective values. + /// + /// This value is used as tolerance to automatically implement [`OptimumReachedProblem`]. + const DELTA: f64 = 1e-8; + + /// Retrieves the known optimum objective value for the optimization problem. fn known_optimum(&self) -> SingleObjective; } + +impl OptimumReachedProblem for P { + fn optimum_reached(&self, objective: SingleObjective) -> bool { + let provided = objective.value(); + let known = self.known_optimum().value(); + debug_assert!( + provided >= known, + "the provided objective value is smaller than the known optimum" + ); + + (provided - known).abs() <= P::DELTA + } +} + +/// The Travelling Salesperson Problem (TSP) optimization problem. +/// +/// TSP is a single-objective optimization problem that involves finding +/// the shortest possible route that visits a given set of vertices and +/// returns to the starting vertex. +/// +/// # Examples +/// +/// A simple implementation of the Travelling Salesperson Problem given a edge weight matrix: +/// +/// ``` +/// use mahf::{Problem, problems::{VectorProblem, TravellingSalespersonProblem}, SingleObjective}; +/// +/// pub struct TSP { +/// pub instance: String, +/// pub edge_weights: Vec>, +/// } +/// +/// impl Problem for TSP { +/// type Encoding = Vec; +/// type Objective = SingleObjective; +/// +/// fn name(&self) -> &str { +/// &self.instance +/// } +/// } +/// +/// impl VectorProblem for TSP { +/// type Element = usize; +/// +/// fn dimension(&self) -> usize { +/// self.edge_weights.len() +/// } +/// } +/// +/// impl TravellingSalespersonProblem for TSP { +/// fn distance(&self, edge: (usize, usize)) -> f64 { +/// let (source, target) = edge; +/// self.edge_weights[source][target] +/// } +/// } +/// ``` +pub trait TravellingSalespersonProblem: + SingleObjectiveProblem + VectorProblem +{ + /// Calculates the distance between two locations, + /// i.e. the edge weight between two vertices identified by their indices. + fn distance(&self, edge: (usize, usize)) -> f64; +} diff --git a/src/problems/objective/mod.rs b/src/problems/objective/mod.rs new file mode 100644 index 00000000..c9411e48 --- /dev/null +++ b/src/problems/objective/mod.rs @@ -0,0 +1,54 @@ +//! Objective types. + +use std::fmt::Debug; + +use thiserror::Error; +use trait_set::trait_set; + +mod multi; +mod single; + +pub use multi::MultiObjective; +pub use single::SingleObjective; + +trait_set! { + /// Collection of traits required by every objective. + /// + /// An objective type is a (lightweight) numerical type with (at least) a partial order. + pub trait AnyObjective = Debug + Clone + Eq + PartialOrd + Send; +} + +/// Marker trait for objective types. +/// +/// The [`SingleObjective`] and [`MultiObjective`] types implement this trait and +/// cover the usual use cases for single- and multi-objective problems. +pub trait Objective: AnyObjective {} + +/// Error type for illegal objective values. +/// Both `NaN` and `-Inf` are considered illegal. +/// +/// # Reasons +/// +/// - `NaN` is not comparable to other objective values, invalidating the required ordering. +/// - `-Inf` would symbolize a infinitely good objective value for minimization, which is not sensible to allow. +/// +/// # Examples +/// +/// Constructing objective values from invalid values fails: +/// +/// ``` +/// use mahf::{MultiObjective, SingleObjective}; +/// +/// assert!(SingleObjective::try_from(f64::NAN).is_err()); +/// assert!(SingleObjective::try_from(f64::NEG_INFINITY).is_err()); +/// +/// assert!(MultiObjective::try_from(vec![f64::NAN, 0.0]).is_err()); +/// assert!(MultiObjective::try_from(vec![0.0, f64::NEG_INFINITY]).is_err()); +/// ``` +#[derive(Debug, Error)] +pub enum IllegalObjective { + #[error("NaN is not a valid objective value")] + NaN, + #[error("Negative infinity is not a valid objective value")] + NegativeInfinity, +} diff --git a/src/problems/objective/multi.rs b/src/problems/objective/multi.rs new file mode 100644 index 00000000..8cdb1d12 --- /dev/null +++ b/src/problems/objective/multi.rs @@ -0,0 +1,199 @@ +//! Objective vector with multiple objectives. + +use std::{cmp::Ordering, fmt::Debug}; + +use serde::Serialize; + +use crate::problems::objective::{IllegalObjective, Objective}; + +/// Represents multiple real-valued objectives and can be used to +/// represent an objective vector in multi-objective optimization. +/// +/// This objective type is used for [`MultiObjectiveProblem`]s. +/// +/// # Restrictions +/// +/// This is a wrapper around [`Vec`], which can't take NaN values, +/// and therefore can implement [`Eq`]. +/// +/// For details, see [`IllegalObjective`]. +/// +/// The [PartialOrd] implementation returns the Pareto ordering. +/// As the Pareto ordering leaves some vectors as incomparable, [`Ord`] is not implemented. +/// +/// [`MultiObjectiveProblem`]: crate::problems::MultiObjectiveProblem +#[derive(Clone, Serialize, PartialEq)] +pub struct MultiObjective(Vec); + +impl MultiObjective { + /// Checks if the objective values contain `+Inf` (positive infinity) or not. + pub fn is_finite(&self) -> bool { + self.0.iter().all(|o| o.is_finite()) + } + + /// Returns the objective value as a slice of floats. + pub fn value(&self) -> &[f64] { + &self.0 + } +} + +impl Debug for MultiObjective { + /// Returns a string representation of the objective values. + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + write!(f, "{:?}", self.0) + } +} + +impl Eq for MultiObjective {} + +impl PartialOrd for MultiObjective { + /// Orders two objective value vectors using Pareto-domination. + /// + /// Note that [Ordering::Less] means that `self` dominates `other`: + /// ``` + /// use std::cmp::Ordering; + /// use mahf::MultiObjective; + /// + /// let a = MultiObjective::try_from(vec![0., 0.]).unwrap(); + /// let b = MultiObjective::try_from(vec![1., 1.]).unwrap(); + /// + /// // `a` dominates `b` (minimization). + /// assert_eq!(a.partial_cmp(&b), Some(Ordering::Less)); + /// ``` + fn partial_cmp(&self, other: &Self) -> Option { + // Use Eq checking for equality + if self == other { + return Some(Ordering::Equal); + } + + // No comparison possible if length doesn't match. + if self.value().len() != other.value().len() { + return None; + } + + // Check all dimensions. + let mut has_better = false; + let mut has_worse = false; + + for (own, other) in self.value().iter().zip(other.value()) { + if own < other { + has_better = true; + } else if own > other { + has_worse = true; + } + } + + match (has_better, has_worse) { + // `self` dominates on all indices. + (true, false) => Some(Ordering::Less), + // `other` dominates on all indices. + (false, true) => Some(Ordering::Greater), + // `self´ and `other` are incomparable. + _ => None, + } + } +} + +impl Objective for MultiObjective {} + +impl From for Vec { + fn from(objective: MultiObjective) -> Self { + objective.0 + } +} + +impl TryFrom> for MultiObjective { + type Error = IllegalObjective; + + /// Tries to convert a vector of floats into a `MultiObjective` value. + /// + /// See [`IllegalObjective`] for more information about illegal values. + fn try_from(value: Vec) -> Result { + match value { + x if x.iter().any(|o| o.is_nan()) => Err(IllegalObjective::NaN), + x if x.iter().any(|o| o.is_infinite() && o.is_sign_negative()) => { + Err(IllegalObjective::NegativeInfinity) + } + _ => Ok(MultiObjective(value)), + } + } +} + +impl TryFrom<&[f64]> for MultiObjective { + type Error = IllegalObjective; + + /// Tries to convert a vector of floats into a `MultiObjective` value. + /// + /// See [`IllegalObjective`] for more information about illegal values. + fn try_from(value: &[f64]) -> Result { + match value { + x if x.iter().any(|o| o.is_nan()) => Err(IllegalObjective::NaN), + x if x.iter().any(|o| o.is_infinite() && o.is_sign_negative()) => { + Err(IllegalObjective::NegativeInfinity) + } + _ => Ok(MultiObjective(value.into())), + } + } +} + +#[cfg(test)] +mod tests { + use std::cmp::Ordering; + + use super::MultiObjective; + + #[test] + fn is_finite_returns_true_for_all_finite() { + assert!(MultiObjective(vec![0., 0.]).is_finite()); + assert!(MultiObjective(vec![-1., 1.]).is_finite()); + } + + #[test] + fn is_finite_returns_false_for_any_infinite() { + assert!(!MultiObjective(vec![0., f64::INFINITY]).is_finite()); + assert!(!MultiObjective(vec![f64::INFINITY, f64::INFINITY]).is_finite()); + } + + #[test] + fn partial_ord_implements_pareto_domination() { + let a = MultiObjective(vec![0., 0.]); + let b = MultiObjective(vec![1., 1.]); + let c = MultiObjective(vec![1., 0.]); + let d = MultiObjective(vec![0., 1.]); + + // All are equal to self. + assert_eq!(a.partial_cmp(&a), Some(Ordering::Equal)); + assert_eq!(b.partial_cmp(&b), Some(Ordering::Equal)); + assert_eq!(c.partial_cmp(&c), Some(Ordering::Equal)); + assert_eq!(d.partial_cmp(&d), Some(Ordering::Equal)); + + // `a` dominates `b`, `c`, and `d`. + assert_eq!(a.partial_cmp(&b), Some(Ordering::Less)); + assert_eq!(a.partial_cmp(&c), Some(Ordering::Less)); + assert_eq!(a.partial_cmp(&d), Some(Ordering::Less)); + + // `b` is dominated by `a`, `c`, and `d`. + assert_eq!(b.partial_cmp(&a), Some(Ordering::Greater)); + assert_eq!(b.partial_cmp(&c), Some(Ordering::Greater)); + assert_eq!(b.partial_cmp(&d), Some(Ordering::Greater)); + + // `c` is dominated by `a`, dominates `b` and incomparable with `d`. + assert_eq!(c.partial_cmp(&a), Some(Ordering::Greater)); + assert_eq!(c.partial_cmp(&b), Some(Ordering::Less)); + assert_eq!(c.partial_cmp(&d), None); + + // `d` is dominated by `a`, dominates `b` and incomparable with `c`. + assert_eq!(d.partial_cmp(&a), Some(Ordering::Greater)); + assert_eq!(d.partial_cmp(&b), Some(Ordering::Less)); + assert_eq!(d.partial_cmp(&c), None); + } + + #[test] + fn try_from_invalid_is_err() { + assert!(MultiObjective::try_from(vec![f64::NAN, 0.]).is_err()); + assert!(MultiObjective::try_from(vec![f64::NAN, f64::NEG_INFINITY]).is_err()); + + assert!(MultiObjective::try_from(vec![f64::NEG_INFINITY, 0.]).is_err()); + assert!(MultiObjective::try_from(vec![f64::NEG_INFINITY, f64::NEG_INFINITY]).is_err()); + } +} diff --git a/src/problems/objective/single.rs b/src/problems/objective/single.rs new file mode 100644 index 00000000..ec52b921 --- /dev/null +++ b/src/problems/objective/single.rs @@ -0,0 +1,115 @@ +//! Objective value for a single objective (also called fitness). + +use std::{cmp::Ordering, fmt::Debug}; + +use derive_more::{Add, Mul, Neg, Sub}; +use serde::Serialize; + +use crate::problems::objective::{IllegalObjective, Objective}; + +/// Represents a single real-valued objective (fitness). +/// +/// Can be used to represent a single objective in single-objective optimization +/// or a combined objective in multi-objective optimization. +/// +/// This objective type is used for [`SingleObjectiveProblem`]s. +/// +/// # Restrictions +/// +/// This is a wrapper around [`f64`], which can't take NaN values, +/// and therefore can implement [`Eq`] and [`Ord`]. +/// +/// For details, see [`IllegalObjective`]. +/// +/// [`SingleObjectiveProblem`]: crate::problems::SingleObjectiveProblem +#[derive(Copy, Clone, Serialize, PartialEq, PartialOrd, Add, Sub, Mul, Neg)] +pub struct SingleObjective(f64); + +impl SingleObjective { + /// Checks if the objective is `+Inf` (positive infinity) or not. + pub fn is_finite(&self) -> bool { + self.0.is_finite() + } + + /// Returns the objective value as float. + pub fn value(&self) -> f64 { + self.0 + } +} + +impl Debug for SingleObjective { + /// Returns a string representation of the objective value. + fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { + write!(f, "{:?}", self.0) + } +} + +/// Comparison is sound because the inner value can't be NaN. +/// This can't be derived because the compiler doesn't know this. +impl Eq for SingleObjective {} + +/// Ordering is sound because the inner value can't be NaN. +/// This can't be derived because the compiler doesn't know this. +#[allow(clippy::derive_ord_xor_partial_ord)] +impl Ord for SingleObjective { + fn cmp(&self, other: &Self) -> Ordering { + // `unwrap()` is guaranteed to never fail. + self.partial_cmp(other).unwrap() + } +} + +impl Objective for SingleObjective {} + +impl Default for SingleObjective { + /// Creates an objective type with value [`f64::INFINITY`], which + /// symbolizes an infinitely bad solution (minimization). + fn default() -> Self { + Self(f64::INFINITY) + } +} + +impl From for f64 { + fn from(value: SingleObjective) -> Self { + value.value() + } +} + +impl TryFrom for SingleObjective { + type Error = IllegalObjective; + + /// Tries to convert a float into a [`SingleObjective`] value. + /// + /// See [`IllegalObjective`] for more information about illegal values. + fn try_from(value: f64) -> Result { + match value { + x if x.is_nan() => Err(IllegalObjective::NaN), + x if x.is_infinite() && x.is_sign_negative() => Err(IllegalObjective::NegativeInfinity), + x => Ok(SingleObjective(x)), + } + } +} + +#[cfg(test)] +mod tests { + use super::SingleObjective; + + #[test] + fn is_finite_returns_true_for_finite() { + assert!(SingleObjective(0.).is_finite()); + assert!(SingleObjective(-1.).is_finite()); + assert!(SingleObjective(1.).is_finite()); + assert!(SingleObjective(1e200).is_finite()); + assert!(SingleObjective(-1e200).is_finite()); + } + + #[test] + fn is_finite_returns_false_for_infinite() { + assert!(!SingleObjective(f64::INFINITY).is_finite()); + } + + #[test] + fn try_from_invalid_is_err() { + assert!(SingleObjective::try_from(f64::NAN).is_err()); + assert!(SingleObjective::try_from(f64::NEG_INFINITY).is_err()); + } +} diff --git a/src/problems/tsp/grammars/symmetric.opt.tour.pest b/src/problems/tsp/grammars/symmetric.opt.tour.pest deleted file mode 100644 index 382df2c5..00000000 --- a/src/problems/tsp/grammars/symmetric.opt.tour.pest +++ /dev/null @@ -1,50 +0,0 @@ -// Grammar for parsing Symmetrical TSP Optimal Tour files from tsplib. (.opt.tour files) - -// Allow arbitrary whitespace/tabs in the file. -WHITESPACE = _{ " " | "\t" } -// Allow comments of format /* COMMENT */. Note that blank lines are still not allowed. -// The comment must end the line before the next parsed line. -COMMENT = _{ "/*" ~ (!"*/" ~ ANY)* ~ "*/" ~ NEWLINE? } - -// NAME : berlin52.opt.tour -name = { (!NEWLINE ~ ANY)* } -name_line = _{ "NAME" ~ ":" ~ name ~ NEWLINE } - -// COMMENT: ... -comment = _{ (!NEWLINE ~ ANY)* } -comments = _{ ("COMMENT" ~ ":" ~ comment ~ NEWLINE)* } - -// TYPE: TOUR -type_line = _{ "TYPE" ~ ":" ~ "TOUR" ~ NEWLINE } - -// DIMENSION: 52 -dimension = @{ ASCII_DIGIT+ } -dimension_line = _{ "DIMENSION" ~ ":" ~ dimension ~ NEWLINE } - -// BEST_SOLUTION: xyz -best_solution = @{ (ASCII_DIGIT | "." | "+" | "-" |"e" )+ } -best_solution_line = _{ ("BEST_SOLUTION" ~ ":" ~ best_solution ~ NEWLINE)? } - -// TOUR_SECTION -TOUR_SECTION_DELIMITER = _{ "TOUR_SECTION" ~ NEWLINE } - -// 1 -index = @{ ASCII_DIGIT+ } -tour_section_nodes = { ( index ~ NEWLINE )+ } - -node_section = _{ (TOUR_SECTION_DELIMITER ~ tour_section_nodes)? } - -// EOF -eof = _{ ("-1" ~ NEWLINE)? ~ "EOF"? ~ NEWLINE* } - -opt = { - name_line ~ - comments ~ - type_line ~ - dimension_line ~ - best_solution_line ~ - node_section ~ - eof -} - -file = { SOI ~ opt ~ EOI } \ No newline at end of file diff --git a/src/problems/tsp/grammars/symmetric.tsp.pest b/src/problems/tsp/grammars/symmetric.tsp.pest deleted file mode 100644 index c04670b8..00000000 --- a/src/problems/tsp/grammars/symmetric.tsp.pest +++ /dev/null @@ -1,52 +0,0 @@ -// Grammar for parsing Symmetrical TSP files from tsplib. (.tsplib files) - -// Allow arbitrary whitespace/tabs in the file. -WHITESPACE = _{ " " | "\t" } -// Allow comments of format /* COMMENT */. Note that blank lines are still not allowed. -// The comment must end the line before the next parsed line. -COMMENT = _{ "/*" ~ (!"*/" ~ ANY)* ~ "*/" ~ NEWLINE? } - -// NAME: berlin52 -name = { (!NEWLINE ~ ANY)* } -name_line = _{ "NAME" ~ ":" ~ name ~ NEWLINE } - -// COMMENT: 52 locations in Berlin (Groetschel) -comment = _{ (!NEWLINE ~ ANY)* } -comments = _{ ("COMMENT" ~ ":" ~ comment ~ NEWLINE)* } - -// TYPE: TSP -type_line = _{ "TYPE" ~ ":" ~ "TSP" ~ NEWLINE } - -// DIMENSION: 52 -dimension = @{ ASCII_DIGIT+ } -dimension_line = _{ "DIMENSION" ~ ":" ~ dimension ~ NEWLINE } - -// EDGE_WEIGHT_TYPE: EUC_2D -edge_weight_type = @{ ( !NEWLINE ~ ANY )+ } -edge_weight_types = { "EUC_2D" | "EUC_3D" | "MAN_2D" | "MAN_3D" | "MAX_2D" | "MAX_3D" } -edge_weight_type_line = _{ "EDGE_WEIGHT_TYPE" ~ ":" ~ (&edge_weight_types ~ edge_weight_type ~ NEWLINE) } - -// NODE_COORD_SECTION -NODE_COORD_SECTION_DELIMITER = _{ "NODE_COORD_SECTION" ~ NEWLINE } - -// 1 565.0 575.0 -coord = @{ (ASCII_DIGIT | "." | "+" | "-" |"e" )+ } -index = @{ ASCII_DIGIT+ } -coords = { index ~ coord ~ coord } -node_coord_section_coords = { ( coords ~ NEWLINE )+ } - -// EOF -EOF = _{ "EOF"? ~ NEWLINE* } - -tsp = { - name_line ~ - comments ~ - type_line ~ - dimension_line ~ - edge_weight_type_line ~ - NODE_COORD_SECTION_DELIMITER ~ - node_coord_section_coords ~ - EOF -} - -file = { SOI ~ tsp ~ EOI } diff --git a/src/problems/tsp/mod.rs b/src/problems/tsp/mod.rs deleted file mode 100644 index 291e672a..00000000 --- a/src/problems/tsp/mod.rs +++ /dev/null @@ -1,54 +0,0 @@ -//! This module allows easy access to instances of the traveling salesman problem taken from tsplib. - -use crate::framework::SingleObjective; - -/// Symmetric TSP -pub mod symmetric; -pub use symmetric::{Instances, SymmetricTsp}; - -/// Asymmetric TSP -pub mod asymmetric {} - -type Coordinates = Vec; -type DistanceMeasure = fn(&[f64], &[f64]) -> SingleObjective; -type Dimension = usize; - -pub type Edge = (usize, usize); -pub type Node = usize; -pub type Route = Vec; - -/// Popular distance functions used in TSP. -mod distances { - use crate::framework::SingleObjective; - - pub fn euclidean_distance(a: &[f64], b: &[f64]) -> SingleObjective { - assert_eq!(a.len(), b.len()); - a.iter() - .zip(b.iter()) - .map(|(x, y)| (x - y).powi(2)) - .sum::() - .sqrt() - .try_into() - .unwrap() - } - - pub fn manhattan_distance(a: &[f64], b: &[f64]) -> SingleObjective { - assert_eq!(a.len(), b.len()); - a.iter() - .zip(b.iter()) - .map(|(x, y)| (x - y).abs()) - .sum::() - .try_into() - .unwrap() - } - - pub fn maximum_distance(a: &[f64], b: &[f64]) -> SingleObjective { - assert_eq!(a.len(), b.len()); - a.iter() - .zip(b.iter()) - .map(|(x, y)| (x - y).abs()) - .map(|d| SingleObjective::try_from(d).unwrap()) - .max() - .unwrap() - } -} diff --git a/src/problems/tsp/symmetric.rs b/src/problems/tsp/symmetric.rs deleted file mode 100644 index 71de0ed8..00000000 --- a/src/problems/tsp/symmetric.rs +++ /dev/null @@ -1,462 +0,0 @@ -//! This module contains instances of the symmetric traveling salesman problem. - -use crate::{ - framework::{Individual, SingleObjective}, - problems::{ - tsp::{Coordinates, Dimension, DistanceMeasure, Edge, Route}, - Evaluator, Problem, VectorProblem, - }, - state::{common::EvaluatorInstance, State}, -}; -use anyhow::{anyhow, Error, Result}; -use pest_consume::Parser; - -// Converts the parsing-tree for symmetric TSP that was constructed by `pest` -// into rust-usable data types using the `pest_consume` package. -#[allow(clippy::upper_case_acronyms)] -mod parser { - // Parser for .tsp files - pub(super) mod tsp { - use crate::problems::tsp::{distances, Coordinates, DistanceMeasure, SymmetricTsp}; - use pest_consume::{match_nodes, Error, Parser}; - - type Result = std::result::Result>; - type Node<'i> = pest_consume::Node<'i, Rule, ()>; - - #[derive(Parser)] - #[grammar = "problems/tsp/grammars/symmetric.tsp.pest"] - pub struct TspParser; - - #[pest_consume::parser] - impl TspParser { - pub fn file(input: Node) -> Result { - Ok(match_nodes!(input.into_children(); - [tsp(tsp), _] => tsp, - )) - } - - fn tsp(input: Node) -> Result { - Ok(match_nodes!(input.clone().into_children(); - [ - name(name), - dimension(dimension), - edge_weight_type(distance_measure), - node_coord_section_coords(positions), - ] => { - if dimension != positions.len() { - return Err(input.error("dimension not equal to number of nodes")) - } - SymmetricTsp { - name, - best_solution: None, - dimension, - positions, - distance_measure, - } - }, - )) - } - - fn name(input: Node) -> Result { - Ok(input.as_str().to_string()) - } - - fn dimension(input: Node) -> Result { - input.as_str().parse().map_err(|e| input.error(e)) - } - - fn edge_weight_type(input: Node) -> Result { - Ok(match input.as_str() { - "EUC_2D" => distances::euclidean_distance, - "MAN_2D" => distances::manhattan_distance, - "MAX_2D" => distances::maximum_distance, - _ => unreachable!(), - }) - } - - fn index(input: Node) -> Result { - input.as_str().parse().map_err(|e| input.error(e)) - } - - fn coord(input: Node) -> Result { - input.as_str().parse().map_err(|e| input.error(e)) - } - - fn coords(input: Node) -> Result { - Ok(match_nodes!(input.into_children(); - [index(_i), coord(x), coord(y)] => vec![x, y], - )) - } - - fn node_coord_section_coords(input: Node) -> Result> { - Ok(match_nodes!(input.into_children(); - [coords(c)..] => c.collect(), - )) - } - - #[allow(non_snake_case, unused_variables, dead_code)] - fn EOI(input: Node) -> Result<()> { - Ok(()) - } - } - } - - // Parser for .opt.tour files - pub(super) mod opt { - use crate::problems::tsp::symmetric::TspOptimum; - use crate::{framework::SingleObjective, problems::tsp::Route}; - use pest_consume::{match_nodes, Error, Parser}; - - type Result = std::result::Result>; - type Node<'i> = pest_consume::Node<'i, Rule, ()>; - - #[derive(Parser)] - #[grammar = "problems/tsp/grammars/symmetric.opt.tour.pest"] - pub struct TspOptParser; - - #[pest_consume::parser] - impl TspOptParser { - pub fn file(input: Node) -> Result { - Ok(match_nodes!(input.into_children(); - [opt(opt), _] => opt, - )) - } - - fn opt(input: Node) -> Result { - Ok(match_nodes!(input.clone().into_children(); - // Only fitness value - [ - name(_name), - dimension(_dimension), - best_solution(objective), - ] => { - TspOptimum { - objective, - solution: None, - } - }, - // Tour nodes are present - [ - name(_name), - dimension(dimension), - best_solution(objective), - tour_section_nodes(nodes), - ] => { - if dimension != nodes.len() { - return Err(input.error("dimension not equal to number of nodes")) - } - TspOptimum { - objective, - solution: Some(nodes), - } - }, - )) - } - - fn name(input: Node) -> Result { - Ok(input.as_str().to_string()) - } - - fn dimension(input: Node) -> Result { - input.as_str().parse().map_err(|e| input.error(e)) - } - - fn best_solution(input: Node) -> Result { - input - .as_str() - .parse() - .map_err(|e| input.error(e)) - .map(|f: f64| SingleObjective::try_from(f).unwrap()) - } - - fn index(input: Node) -> Result { - let i: usize = input.as_str().parse().map_err(|e| input.error(e))?; - if i == 0 { - Err(input.error("node index can't be zero")) - } else { - Ok(i - 1) - } - } - - fn tour_section_nodes(input: Node) -> Result { - Ok(match_nodes!(input.into_children(); - [index(i)..] => i.collect::>(), - )) - } - - #[allow(non_snake_case, unused_variables, dead_code)] - fn EOI(input: Node) -> Result<()> { - Ok(()) - } - } - } -} - -/// This enum represents built in instances of the symmetric travelling salesman problem. -#[rustfmt::skip] -#[derive(Debug)] -pub enum Instances { - A280, - BERLIN52, - BIER127, - CH130, CH150, - D198, D493, D657, D1291, D1655, D2103, D15112, D18512, - EIL51, EIL76, EIL101, - FL417, FL1400, FL1577, FL3795, - NRW1379, - USA13509, -} - -impl std::fmt::Display for Instances { - fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { - std::fmt::Debug::fmt(self, f) - } -} - -impl TryFrom<&str> for Instances { - type Error = Error; - - fn try_from(value: &str) -> Result { - match value.to_uppercase().as_str() { - "A280" => Ok(Instances::A280), - "BERLIN52" => Ok(Instances::BERLIN52), - "BIER127" => Ok(Instances::BIER127), - "CH130" => Ok(Instances::CH130), - "CH150" => Ok(Instances::CH150), - "D198" => Ok(Instances::D198), - "D493" => Ok(Instances::D493), - "D657" => Ok(Instances::D657), - "D1291" => Ok(Instances::D1291), - "D1655" => Ok(Instances::D1655), - "D2103" => Ok(Instances::D2103), - "D15112" => Ok(Instances::D15112), - "D18512" => Ok(Instances::D18512), - "EIL51" => Ok(Instances::EIL51), - "EIL76" => Ok(Instances::EIL76), - "EIL101" => Ok(Instances::EIL101), - "FL417" => Ok(Instances::FL417), - "FL1400" => Ok(Instances::FL1400), - "FL1577" => Ok(Instances::FL1577), - "FL3795" => Ok(Instances::FL3795), - "NRW1379" => Ok(Instances::NRW1379), - "USA13509" => Ok(Instances::USA13509), - _ => Err(anyhow!("Unknown instance {}", value)), - } - } -} - -impl Instances { - /// Tries to load the built-in instance. - pub fn try_load(&self) -> Result { - #[rustfmt::skip] - let (data, opt) = match self { - Self::A280 => (include_str!("./tsplib/a280.tsp"), Some(include_str!("./tsplib/a280.opt.tour"))), - Self::BERLIN52 => (include_str!("./tsplib/berlin52.tsp"), Some(include_str!("./tsplib/berlin52.opt.tour"))), - Self::BIER127 => (include_str!("./tsplib/bier127.tsp"), Some(include_str!("./tsplib/bier127.opt.tour"))), - Self::CH130 => (include_str!("./tsplib/ch130.tsp"), Some(include_str!("./tsplib/ch130.opt.tour"))), - Self::CH150 => (include_str!("./tsplib/ch150.tsp"), Some(include_str!("./tsplib/ch150.opt.tour"))), - Self::D198 => (include_str!("./tsplib/d198.tsp"), Some(include_str!("./tsplib/d198.opt.tour"))), - Self::D493 => (include_str!("./tsplib/d493.tsp"), Some(include_str!("./tsplib/d493.opt.tour"))), - Self::D657 => (include_str!("./tsplib/d657.tsp"), Some(include_str!("./tsplib/d657.opt.tour"))), - Self::D1291 => (include_str!("./tsplib/d1291.tsp"), Some(include_str!("./tsplib/d1291.opt.tour"))), - Self::D1655 => (include_str!("./tsplib/d1655.tsp"), Some(include_str!("./tsplib/d1655.opt.tour"))), - Self::D2103 => (include_str!("./tsplib/d2103.tsp"), Some(include_str!("./tsplib/d2103.opt.tour"))), - Self::D15112 => (include_str!("./tsplib/d15112.tsp"), Some(include_str!("./tsplib/d15112.opt.tour"))), - Self::D18512 => (include_str!("./tsplib/d18512.tsp"), Some(include_str!("./tsplib/d18512.opt.tour"))), - Self::EIL51 => (include_str!("./tsplib/eil51.tsp"), Some(include_str!("./tsplib/eil51.opt.tour"))), - Self::EIL76 => (include_str!("./tsplib/eil76.tsp"), Some(include_str!("./tsplib/eil76.opt.tour"))), - Self::EIL101 => (include_str!("./tsplib/eil101.tsp"), Some(include_str!("./tsplib/eil101.opt.tour"))), - Self::FL417 => (include_str!("./tsplib/fl417.tsp"), Some(include_str!("./tsplib/fl417.opt.tour"))), - Self::FL1400 => (include_str!("./tsplib/fl1400.tsp"), Some(include_str!("./tsplib/fl1400.opt.tour"))), - Self::FL1577 => (include_str!("./tsplib/fl1577.tsp"), Some(include_str!("./tsplib/fl1577.opt.tour"))), - Self::FL3795 => (include_str!("./tsplib/fl3795.tsp"), Some(include_str!("./tsplib/fl3795.opt.tour"))), - Self::NRW1379 => (include_str!("./tsplib/nrw1379.tsp"), Some(include_str!("./tsplib/nrw1379.opt.tour"))), - Self::USA13509 => (include_str!("./tsplib/usa13509.tsp"), Some(include_str!("./tsplib/usa13509.opt.tour"))), - }; - // Update name with enum if tsplib name is empty - let mut tsp = SymmetricTsp::try_parse(data, opt)?; - if tsp.name.is_empty() { - tsp.name = self.to_string(); - } - Ok(tsp) - } - - /// Loads the built-in instance. This method should by design never panic. - pub fn load(&self) -> SymmetricTsp { - self.try_load().expect("Error while constructing instance") - } -} - -/// Represents the (global) optimum of the search space. -#[derive(Debug, Clone, serde::Serialize)] -pub struct TspOptimum { - pub objective: SingleObjective, - pub solution: Option, -} - -/// Represents an instance of the symmetric travelling salesman problem. -#[derive(serde::Serialize)] -pub struct SymmetricTsp { - /// Name of the instance - pub name: String, - /// Dimension of the instance - pub dimension: Dimension, - /// Best possible solution - pub best_solution: Option, - /// The cities coordinates - #[serde(skip)] - pub positions: Vec, - /// How distance should be computed - #[serde(skip)] - distance_measure: DistanceMeasure, -} - -struct SymmetricTspEvaluator; - -impl Evaluator for SymmetricTspEvaluator { - type Problem = SymmetricTsp; - - fn evaluate( - &mut self, - problem: &Self::Problem, - _state: &mut State, - individuals: &mut [Individual], - ) { - for individual in individuals { - individual.evaluate(problem.evaluate_solution(individual.solution())); - } - } -} - -impl SymmetricTsp { - fn evaluate_solution(&self, solution: &Route) -> SingleObjective { - solution - .iter() - .copied() - .zip(solution.iter().copied().skip(1)) - .map(|edge| self.distance(edge)) - .sum::() - .try_into() - .unwrap() - } -} - -impl Problem for SymmetricTsp { - type Encoding = Route; - type Objective = SingleObjective; - - fn name(&self) -> &str { - "SymmetricTsp" - } - - fn default_evaluator<'a>(&self) -> EvaluatorInstance<'a, Self> { - EvaluatorInstance::functional(|problem, _state, individuals| { - for individual in individuals { - individual.evaluate(problem.evaluate_solution(individual.solution())); - } - }) - } -} - -impl VectorProblem for SymmetricTsp { - type T = usize; - - fn dimension(&self) -> usize { - self.dimension - } -} - -impl SymmetricTsp { - pub fn best_fitness(&self) -> Option { - self.best_solution.as_ref().map(|o| o.objective.value()) - } - - /// Returns the weight/distance of the given edge. - pub fn distance(&self, edge: Edge) -> f64 { - let (a, b) = edge; - (self.distance_measure)(&self.positions[a], &self.positions[b]).into() - } - - /// Greedily constructs a Route, always taking the shortest edge. - pub fn greedy_route(&self) -> Route { - let mut route = Vec::with_capacity(self.dimension); - let mut remaining = (1..self.dimension).into_iter().collect::>(); - route.push(0); - while !remaining.is_empty() { - let last = *route.last().unwrap(); - let next_index = remaining - .iter() - .enumerate() - .min_by_key(|(_, &r)| SingleObjective::try_from(self.distance((last, r))).unwrap()) - .unwrap() - .0; - let next = remaining.remove(next_index); - route.push(next); - } - route - } - - /// This method constructs a TSP instance from a string representation (`data`) and an optional solution (`opt`). - /// There is no good reason to call it directly, just use `SymmetricTspInstances.try_load()` instead. - fn try_parse(data: &str, opt: Option<&str>) -> Result { - let input = parser::tsp::TspParser::parse(parser::tsp::Rule::file, data) - .map_err(|e| Error::new(e).context("error while parsing .tsp file"))? - .single() - .unwrap(); - let mut tsp = parser::tsp::TspParser::file(input) - .map_err(|e| Error::new(e).context("tsp data type conversions failed"))?; - - if let Some(opt) = opt { - let opt_input = parser::opt::TspOptParser::parse(parser::opt::Rule::file, opt) - .map_err(|e| Error::new(e).context("error while parsing .opt.tour file"))? - .single() - .unwrap(); - let opt = parser::opt::TspOptParser::file(opt_input) - .map_err(|e| Error::new(e).context("tsp opt tour data type conversions failed"))?; - if let Some(sol) = &opt.solution { - if sol.len() != tsp.dimension { - return Err(anyhow!("dimension of opt does not match problem dimension",)); - } - } - tsp.best_solution = Some(opt); - } - - Ok(tsp) - } -} - -#[cfg(test)] -mod tests { - use super::*; - use float_eq::assert_float_eq; - - #[test] - fn loading_berlin52() { - let opt_tour = vec![ - 0, 48, 31, 44, 18, 40, 7, 8, 9, 42, 32, 50, 10, 51, 13, 12, 46, 25, 26, 27, 11, 24, 3, - 5, 14, 4, 23, 47, 37, 36, 39, 38, 35, 34, 33, 43, 45, 15, 28, 49, 19, 22, 29, 1, 6, 41, - 20, 16, 2, 17, 30, 21, - ]; - let tsp = Instances::BERLIN52.load(); - let best_solution = tsp.best_solution.unwrap(); - - assert_eq!(tsp.dimension, 52); - assert_float_eq!(best_solution.objective.value(), 7542.0, ulps <= 2); - assert_eq!(best_solution.solution.unwrap(), opt_tour); - } - - #[test] - fn evaluating_berlin52() { - let tsp = Instances::BERLIN52.load(); - let best = tsp.best_solution.as_ref().unwrap(); - - // There seems to be a difference between the best solutions supplied in BEST_SOLUTION - // and the evaluated routes from the opt.tour files. Is this due to rounding errors? - assert_float_eq!( - tsp.evaluate_solution(best.solution.as_ref().unwrap()) - .value(), - best.objective.value(), - abs <= 50.0 - ); - } -} diff --git a/src/problems/tsp/tsplib/README.md b/src/problems/tsp/tsplib/README.md deleted file mode 100644 index e3cee2bb..00000000 --- a/src/problems/tsp/tsplib/README.md +++ /dev/null @@ -1,13 +0,0 @@ -# TSPLIB - -## Symmetric traveling salesman problems. - -These are from the [Heidelberg University](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/). - -### Modifications - -Each `opt.tour` file has been extended with a `BEST_SOLUTION` attribute. The best solution for each problem in TSPLIB can be -found [here](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/STSP.html). -The lower bound was used instead if no best solution is currently known. - -The parser currently only supports 2D weights with `EUC_2D`, `MAN_2D` or `MAX_2D` weights. diff --git a/src/problems/tsp/tsplib/a280.opt.tour b/src/problems/tsp/tsplib/a280.opt.tour deleted file mode 100644 index d67e015b..00000000 --- a/src/problems/tsp/tsplib/a280.opt.tour +++ /dev/null @@ -1,287 +0,0 @@ -NAME : ./TSPLIB/a280.tsp.optbc.tour -TYPE : TOUR -DIMENSION : 280 -BEST_SOLUTION: 2579 -TOUR_SECTION -1 -2 -242 -243 -244 -241 -240 -239 -238 -237 -236 -235 -234 -233 -232 -231 -246 -245 -247 -250 -251 -230 -229 -228 -227 -226 -225 -224 -223 -222 -221 -220 -219 -218 -217 -216 -215 -214 -213 -212 -211 -210 -207 -206 -205 -204 -203 -202 -201 -198 -197 -196 -195 -194 -193 -192 -191 -190 -189 -188 -187 -186 -185 -184 -183 -182 -181 -176 -180 -179 -150 -178 -177 -151 -152 -156 -153 -155 -154 -129 -130 -131 -20 -21 -128 -127 -126 -125 -124 -123 -122 -121 -120 -119 -157 -158 -159 -160 -175 -161 -162 -163 -164 -165 -166 -167 -168 -169 -170 -172 -171 -173 -174 -107 -106 -105 -104 -103 -102 -101 -100 -99 -98 -97 -96 -95 -94 -93 -92 -91 -90 -89 -109 -108 -110 -111 -112 -88 -87 -113 -114 -115 -117 -116 -86 -85 -84 -83 -82 -81 -80 -79 -78 -77 -76 -75 -74 -73 -72 -71 -70 -69 -68 -67 -66 -65 -64 -58 -57 -56 -55 -54 -53 -52 -51 -50 -49 -48 -47 -46 -45 -44 -59 -63 -62 -118 -61 -60 -43 -42 -41 -40 -39 -38 -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 -26 -22 -25 -23 -24 -14 -15 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -277 -276 -275 -274 -273 -272 -271 -16 -17 -18 -19 -132 -133 -134 -270 -269 -135 -136 -268 -267 -137 -138 -139 -149 -148 -147 -146 -145 -199 -200 -144 -143 -142 -141 -140 -266 -265 -264 -263 -262 -261 -260 -259 -258 -257 -254 -253 -208 -209 -252 -255 -256 -249 -248 -278 -279 -3 -280 --1 -EOF diff --git a/src/problems/tsp/tsplib/a280.tsp b/src/problems/tsp/tsplib/a280.tsp deleted file mode 100644 index 40e506c8..00000000 --- a/src/problems/tsp/tsplib/a280.tsp +++ /dev/null @@ -1,287 +0,0 @@ -NAME : a280 -COMMENT : drilling problem (Ludwig) -TYPE : TSP -DIMENSION: 280 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION - 1 288 149 - 2 288 129 - 3 270 133 - 4 256 141 - 5 256 157 - 6 246 157 - 7 236 169 - 8 228 169 - 9 228 161 - 10 220 169 - 11 212 169 - 12 204 169 - 13 196 169 - 14 188 169 - 15 196 161 - 16 188 145 - 17 172 145 - 18 164 145 - 19 156 145 - 20 148 145 - 21 140 145 - 22 148 169 - 23 164 169 - 24 172 169 - 25 156 169 - 26 140 169 - 27 132 169 - 28 124 169 - 29 116 161 - 30 104 153 - 31 104 161 - 32 104 169 - 33 90 165 - 34 80 157 - 35 64 157 - 36 64 165 - 37 56 169 - 38 56 161 - 39 56 153 - 40 56 145 - 41 56 137 - 42 56 129 - 43 56 121 - 44 40 121 - 45 40 129 - 46 40 137 - 47 40 145 - 48 40 153 - 49 40 161 - 50 40 169 - 51 32 169 - 52 32 161 - 53 32 153 - 54 32 145 - 55 32 137 - 56 32 129 - 57 32 121 - 58 32 113 - 59 40 113 - 60 56 113 - 61 56 105 - 62 48 99 - 63 40 99 - 64 32 97 - 65 32 89 - 66 24 89 - 67 16 97 - 68 16 109 - 69 8 109 - 70 8 97 - 71 8 89 - 72 8 81 - 73 8 73 - 74 8 65 - 75 8 57 - 76 16 57 - 77 8 49 - 78 8 41 - 79 24 45 - 80 32 41 - 81 32 49 - 82 32 57 - 83 32 65 - 84 32 73 - 85 32 81 - 86 40 83 - 87 40 73 - 88 40 63 - 89 40 51 - 90 44 43 - 91 44 35 - 92 44 27 - 93 32 25 - 94 24 25 - 95 16 25 - 96 16 17 - 97 24 17 - 98 32 17 - 99 44 11 -100 56 9 -101 56 17 -102 56 25 -103 56 33 -104 56 41 -105 64 41 -106 72 41 -107 72 49 -108 56 49 -109 48 51 -110 56 57 -111 56 65 -112 48 63 -113 48 73 -114 56 73 -115 56 81 -116 48 83 -117 56 89 -118 56 97 -119 104 97 -120 104 105 -121 104 113 -122 104 121 -123 104 129 -124 104 137 -125 104 145 -126 116 145 -127 124 145 -128 132 145 -129 132 137 -130 140 137 -131 148 137 -132 156 137 -133 164 137 -134 172 125 -135 172 117 -136 172 109 -137 172 101 -138 172 93 -139 172 85 -140 180 85 -141 180 77 -142 180 69 -143 180 61 -144 180 53 -145 172 53 -146 172 61 -147 172 69 -148 172 77 -149 164 81 -150 148 85 -151 124 85 -152 124 93 -153 124 109 -154 124 125 -155 124 117 -156 124 101 -157 104 89 -158 104 81 -159 104 73 -160 104 65 -161 104 49 -162 104 41 -163 104 33 -164 104 25 -165 104 17 -166 92 9 -167 80 9 -168 72 9 -169 64 21 -170 72 25 -171 80 25 -172 80 25 -173 80 41 -174 88 49 -175 104 57 -176 124 69 -177 124 77 -178 132 81 -179 140 65 -180 132 61 -181 124 61 -182 124 53 -183 124 45 -184 124 37 -185 124 29 -186 132 21 -187 124 21 -188 120 9 -189 128 9 -190 136 9 -191 148 9 -192 162 9 -193 156 25 -194 172 21 -195 180 21 -196 180 29 -197 172 29 -198 172 37 -199 172 45 -200 180 45 -201 180 37 -202 188 41 -203 196 49 -204 204 57 -205 212 65 -206 220 73 -207 228 69 -208 228 77 -209 236 77 -210 236 69 -211 236 61 -212 228 61 -213 228 53 -214 236 53 -215 236 45 -216 228 45 -217 228 37 -218 236 37 -219 236 29 -220 228 29 -221 228 21 -222 236 21 -223 252 21 -224 260 29 -225 260 37 -226 260 45 -227 260 53 -228 260 61 -229 260 69 -230 260 77 -231 276 77 -232 276 69 -233 276 61 -234 276 53 -235 284 53 -236 284 61 -237 284 69 -238 284 77 -239 284 85 -240 284 93 -241 284 101 -242 288 109 -243 280 109 -244 276 101 -245 276 93 -246 276 85 -247 268 97 -248 260 109 -249 252 101 -250 260 93 -251 260 85 -252 236 85 -253 228 85 -254 228 93 -255 236 93 -256 236 101 -257 228 101 -258 228 109 -259 228 117 -260 228 125 -261 220 125 -262 212 117 -263 204 109 -264 196 101 -265 188 93 -266 180 93 -267 180 101 -268 180 109 -269 180 117 -270 180 125 -271 196 145 -272 204 145 -273 212 145 -274 220 145 -275 228 145 -276 236 145 -277 246 141 -278 252 125 -279 260 129 -280 280 133 -EOF diff --git a/src/problems/tsp/tsplib/berlin52.opt.tour b/src/problems/tsp/tsplib/berlin52.opt.tour deleted file mode 100644 index 1bf26e7e..00000000 --- a/src/problems/tsp/tsplib/berlin52.opt.tour +++ /dev/null @@ -1,59 +0,0 @@ -NAME : berlin52.opt.tour -TYPE : TOUR -DIMENSION : 52 -BEST_SOLUTION: 7542 -TOUR_SECTION -1 -49 -32 -45 -19 -41 -8 -9 -10 -43 -33 -51 -11 -52 -14 -13 -47 -26 -27 -28 -12 -25 -4 -6 -15 -5 -24 -48 -38 -37 -40 -39 -36 -35 -34 -44 -46 -16 -29 -50 -20 -23 -30 -2 -7 -42 -21 -17 -3 -18 -31 -22 --1 -EOF diff --git a/src/problems/tsp/tsplib/berlin52.tsp b/src/problems/tsp/tsplib/berlin52.tsp deleted file mode 100644 index a58381cf..00000000 --- a/src/problems/tsp/tsplib/berlin52.tsp +++ /dev/null @@ -1,59 +0,0 @@ -NAME: BERLIN52 -COMMENT: 52 locations in Berlin (Groetschel) -TYPE: TSP -DIMENSION: 52 -EDGE_WEIGHT_TYPE: EUC_2D -NODE_COORD_SECTION -1 565.0 575.0 -2 25.0 185.0 -3 345.0 750.0 -4 945.0 685.0 -5 845.0 655.0 -6 880.0 660.0 -7 25.0 230.0 -8 525.0 1000.0 -9 580.0 1175.0 -10 650.0 1130.0 -11 1605.0 620.0 -12 1220.0 580.0 -13 1465.0 200.0 -14 1530.0 5.0 -15 845.0 680.0 -16 725.0 370.0 -17 145.0 665.0 -18 415.0 635.0 -19 510.0 875.0 -20 560.0 365.0 -21 300.0 465.0 -22 520.0 585.0 -23 480.0 415.0 -24 835.0 625.0 -25 975.0 580.0 -26 1215.0 245.0 -27 1320.0 315.0 -28 1250.0 400.0 -29 660.0 180.0 -30 410.0 250.0 -31 420.0 555.0 -32 575.0 665.0 -33 1150.0 1160.0 -34 700.0 580.0 -35 685.0 595.0 -36 685.0 610.0 -37 770.0 610.0 -38 795.0 645.0 -39 720.0 635.0 -40 760.0 650.0 -41 475.0 960.0 -42 95.0 260.0 -43 875.0 920.0 -44 700.0 500.0 -45 555.0 815.0 -46 830.0 485.0 -47 1170.0 65.0 -48 830.0 610.0 -49 605.0 625.0 -50 595.0 360.0 -51 1340.0 725.0 -52 1740.0 245.0 -EOF diff --git a/src/problems/tsp/tsplib/bier127.opt.tour b/src/problems/tsp/tsplib/bier127.opt.tour deleted file mode 100644 index 82fd304a..00000000 --- a/src/problems/tsp/tsplib/bier127.opt.tour +++ /dev/null @@ -1,5 +0,0 @@ -NAME: bier127 -TYPE: TOUR -DIMENSION: 127 -BEST_SOLUTION: 118282 -EOF diff --git a/src/problems/tsp/tsplib/bier127.tsp b/src/problems/tsp/tsplib/bier127.tsp deleted file mode 100644 index 2f6ef7d2..00000000 --- a/src/problems/tsp/tsplib/bier127.tsp +++ /dev/null @@ -1,134 +0,0 @@ -NAME: bier127 -COMMENT: 127 Biergaerten in Augsburg (Juenger/Reinelt) -TYPE: TSP -DIMENSION: 127 -EDGE_WEIGHT_TYPE: EUC_2D -NODE_COORD_SECTION -1 9860 14152 -2 9396 14616 -3 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a/src/problems/tsp/tsplib/ch130.opt.tour b/src/problems/tsp/tsplib/ch130.opt.tour deleted file mode 100644 index 7be69b20..00000000 --- a/src/problems/tsp/tsplib/ch130.opt.tour +++ /dev/null @@ -1,137 +0,0 @@ -NAME : ch130.opt.tour -COMMENT : Length 6110 -TYPE : TOUR -DIMENSION : 130 -BEST_SOLUTION: 6110 -TOUR_SECTION -1 -41 -39 -117 -112 -115 -28 -62 -105 -128 -16 -45 -5 -11 -76 -109 -61 -129 -124 -64 -69 -86 -88 -26 -7 -97 -70 -107 -127 -104 -43 -34 -17 -31 -27 -19 -100 -15 -29 -24 -116 -95 -79 -87 -12 -81 -103 -77 -94 -89 -110 -98 -68 -63 -48 -25 -113 -32 -36 -84 -119 -111 -123 -101 -82 -57 -9 -56 -65 -52 -75 -74 -99 -73 -92 -38 -106 -53 -120 -58 -49 -72 -91 -6 -102 -10 -14 -67 -13 -96 -122 -55 -60 -51 -42 -44 -93 -37 -22 -47 -40 -23 -33 -21 -126 -121 -78 -66 -85 -125 -90 -59 -30 -83 -3 -114 -108 -8 -18 -46 -80 -118 -20 -4 -35 -54 -2 -50 -130 -71 --1 diff --git a/src/problems/tsp/tsplib/ch130.tsp b/src/problems/tsp/tsplib/ch130.tsp deleted file mode 100644 index ab0671a5..00000000 --- a/src/problems/tsp/tsplib/ch130.tsp +++ /dev/null @@ -1,137 +0,0 @@ -NAME: ch130 -COMMENT: 130 city problem (Churritz) -TYPE: TSP -DIMENSION: 130 -EDGE_WEIGHT_TYPE: EUC_2D -NODE_COORD_SECTION -1 334.5909245845 161.7809319139 -2 397.6446634067 262.8165330708 -3 503.8741827107 172.8741151168 -4 444.0479403502 384.6491809647 -5 311.6137146746 2.0091699828 -6 662.8551011379 549.2301263653 -7 40.0979030612 187.2375430791 -8 526.8941409181 215.7079092185 -9 209.1887938487 691.0262291948 -10 683.2674131973 414.2096286906 -11 280.7494438748 5.9206392047 -12 252.7493090080 535.7430385019 -13 698.7850451923 348.4413729766 -14 678.7574678104 410.7256424438 -15 220.0041131179 409.1225812873 -16 355.1528556851 76.3912076444 -17 296.9724227786 313.1312792361 -18 504.5154071733 240.8866564499 -19 224.1079496785 358.4872228907 -20 470.6801296968 309.6259188406 -21 554.2530513223 279.4242466521 -22 567.6332684419 352.7162027273 -23 599.0532671093 361.0948690386 -24 240.5232959211 430.6036007844 -25 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48.0798124069 270.9680673720 -150 91.6467647724 166.3541158474 -EOF diff --git a/src/problems/tsp/tsplib/d1291.opt.tour b/src/problems/tsp/tsplib/d1291.opt.tour deleted file mode 100644 index 6f34cbb0..00000000 --- a/src/problems/tsp/tsplib/d1291.opt.tour +++ /dev/null @@ -1,5 +0,0 @@ -NAME : d1291 -TYPE : TOUR -DIMENSION : 1291 -BEST_SOLUTION: 50801 -EOF diff --git a/src/problems/tsp/tsplib/d1291.tsp b/src/problems/tsp/tsplib/d1291.tsp deleted file mode 100644 index da8c26cb..00000000 --- a/src/problems/tsp/tsplib/d1291.tsp +++ /dev/null @@ -1,1298 +0,0 @@ -NAME : d1291 -COMMENT : Drilling problem (Reinelt) -TYPE : TSP -DIMENSION : 1291 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 0.00000e+00 0.00000e+00 -2 8.37000e+02 9.58300e+02 -3 8.62400e+02 9.58300e+02 -4 8.87800e+02 9.58300e+02 -5 9.13200e+02 9.58300e+02 -6 9.38600e+02 9.58300e+02 -7 9.64000e+02 9.58300e+02 -8 9.89400e+02 9.58300e+02 -9 1.01480e+03 9.58300e+02 -10 1.04020e+03 9.58300e+02 -11 1.06560e+03 9.58300e+02 -12 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b/src/problems/tsp/tsplib/d2103.opt.tour deleted file mode 100644 index bb7ba59a..00000000 --- a/src/problems/tsp/tsplib/d2103.opt.tour +++ /dev/null @@ -1,6 +0,0 @@ -NAME : d2103 -TYPE : TOUR -DIMENSION : 2103 -/* BEST_SOLUTION is lower bound from [79952,80450] */ -BEST_SOLUTION: 79952 -EOF diff --git a/src/problems/tsp/tsplib/d2103.tsp b/src/problems/tsp/tsplib/d2103.tsp deleted file mode 100644 index 067116a9..00000000 --- a/src/problems/tsp/tsplib/d2103.tsp +++ /dev/null @@ -1,2110 +0,0 @@ -NAME : d2103 -COMMENT : Drilling problem (Reinelt) -TYPE : TSP -DIMENSION : 2103 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 0.00000e+00 0.00000e+00 -2 6.91100e+02 8.56700e+02 -3 7.41900e+02 8.56700e+02 -4 7.92700e+02 8.56700e+02 -5 8.68900e+02 8.63100e+02 -6 7.92700e+02 9.07500e+02 -7 6.91100e+02 9.07500e+02 -8 8.68900e+02 9.13900e+02 -9 7.92700e+02 9.58300e+02 -10 7.41900e+02 9.58300e+02 -11 6.91100e+02 9.58300e+02 -12 6.91100e+02 1.61870e+03 -13 7.41900e+02 1.61870e+03 -14 7.92700e+02 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4.01830e+03 3.24430e+03 -2103 4.06910e+03 3.24430e+03 -EOF diff --git a/src/problems/tsp/tsplib/d493.opt.tour b/src/problems/tsp/tsplib/d493.opt.tour deleted file mode 100644 index 97ca1898..00000000 --- a/src/problems/tsp/tsplib/d493.opt.tour +++ /dev/null @@ -1,5 +0,0 @@ -NAME : d493 -TYPE : TOUR -DIMENSION : 493 -BEST_SOLUTION: 35002 -EOF diff --git a/src/problems/tsp/tsplib/d493.tsp b/src/problems/tsp/tsplib/d493.tsp deleted file mode 100644 index d3795227..00000000 --- a/src/problems/tsp/tsplib/d493.tsp +++ /dev/null @@ -1,500 +0,0 @@ -NAME : d493 -COMMENT : Drilling problem (Reinelt) -TYPE : TSP -DIMENSION : 493 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 0.00000e+00 0.00000e+00 -2 1.11630e+03 1.55520e+03 -3 1.35760e+03 1.47900e+03 -4 1.14810e+03 1.77110e+03 -5 1.18620e+03 1.79650e+03 -6 1.20520e+03 1.88540e+03 -7 1.23700e+03 1.99340e+03 -8 1.30050e+03 2.00610e+03 -9 1.16080e+03 2.02510e+03 -10 1.17350e+03 2.03780e+03 -11 1.22430e+03 2.05050e+03 -12 1.35130e+03 2.24100e+03 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b/src/problems/tsp/tsplib/d657.opt.tour deleted file mode 100644 index af08d4cb..00000000 --- a/src/problems/tsp/tsplib/d657.opt.tour +++ /dev/null @@ -1,5 +0,0 @@ -NAME : d657 -TYPE : TOUR -DIMENSION : 657 -BEST_SOLUTION: 48912 -EOF diff --git a/src/problems/tsp/tsplib/d657.tsp b/src/problems/tsp/tsplib/d657.tsp deleted file mode 100644 index 09e01233..00000000 --- a/src/problems/tsp/tsplib/d657.tsp +++ /dev/null @@ -1,664 +0,0 @@ -NAME : d657 -COMMENT : Drilling problem (Reinelt) -TYPE : TSP -DIMENSION : 657 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 0.00000e+00 0.00000e+00 -2 8.75100e+02 9.83700e+02 -3 8.87800e+02 1.04090e+03 -4 8.75100e+02 1.13610e+03 -5 8.87800e+02 1.27580e+03 -6 8.87800e+02 1.49810e+03 -7 9.64000e+02 1.50440e+03 -8 1.11640e+03 1.21230e+03 -9 1.01480e+03 1.14880e+03 -10 1.05290e+03 1.13610e+03 -11 1.01480e+03 1.05990e+03 -12 1.11640e+03 1.05990e+03 -13 1.16720e+03 1.01550e+03 -14 1.49740e+03 1.04090e+03 -15 1.34500e+03 1.03450e+03 -16 1.37040e+03 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3.63100e+03 1.14880e+03 -638 3.61830e+03 1.13610e+03 -639 3.59920e+03 1.12340e+03 -640 3.58650e+03 1.11070e+03 -641 3.61830e+03 1.09170e+03 -642 3.63100e+03 1.07900e+03 -643 3.65640e+03 1.01550e+03 -644 3.83420e+03 9.83700e+02 -645 3.85960e+03 9.83700e+02 -646 3.94850e+03 9.96400e+02 -647 3.96750e+03 9.71000e+02 -648 3.92940e+03 1.30120e+03 -649 4.00560e+03 1.39010e+03 -650 3.85960e+03 1.42820e+03 -651 3.81510e+03 1.46000e+03 -652 3.78340e+03 1.48540e+03 -653 3.96750e+03 1.54250e+03 -654 3.96750e+03 1.71400e+03 -655 3.88500e+03 1.79650e+03 -656 3.88500e+03 1.84730e+03 -657 3.94210e+03 2.03780e+03 -EOF diff --git a/src/problems/tsp/tsplib/eil101.opt.tour b/src/problems/tsp/tsplib/eil101.opt.tour deleted file mode 100644 index 1b241695..00000000 --- a/src/problems/tsp/tsplib/eil101.opt.tour +++ /dev/null @@ -1,109 +0,0 @@ -NAME : eil101.opt.tour -COMMENT : Optimum tour for eil101.tsp (Length 629) -TYPE : TOUR -DIMENSION : 101 -BEST_SOLUTION: 629 -TOUR_SECTION -1 -69 -27 -101 -53 -28 -26 -12 -80 -68 -29 -24 -54 -55 -25 -4 -39 -67 -23 -56 -75 -41 -22 -74 -72 -73 -21 -40 -58 -13 -94 -95 -97 -87 -2 -57 -15 -43 -42 -14 -44 -38 -86 -16 -61 -85 -91 -100 -98 -37 -92 -59 -93 -99 -96 -6 -89 -52 -18 -83 -60 -5 -84 -17 -45 -8 -46 -47 -36 -49 -64 -63 -90 -32 -10 -62 -11 -19 -48 -82 -7 -88 -31 -70 -30 -20 -66 -71 -65 -35 -34 -78 -81 -9 -51 -33 -79 -3 -77 -76 -50 --1 -EOF diff --git a/src/problems/tsp/tsplib/eil101.tsp b/src/problems/tsp/tsplib/eil101.tsp deleted file mode 100644 index 9672f849..00000000 --- a/src/problems/tsp/tsplib/eil101.tsp +++ /dev/null @@ -1,108 +0,0 @@ -NAME : eil101 -COMMENT : 101-city problem (Christofides/Eilon) -TYPE : TSP -DIMENSION : 101 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 41 49 -2 35 17 -3 55 45 -4 55 20 -5 15 30 -6 25 30 -7 20 50 -8 10 43 -9 55 60 -10 30 60 -11 20 65 -12 50 35 -13 30 25 -14 15 10 -15 30 5 -16 10 20 -17 5 30 -18 20 40 -19 15 60 -20 45 65 -21 45 20 -22 45 10 -23 55 5 -24 65 35 -25 65 20 -26 45 30 -27 35 40 -28 41 37 -29 64 42 -30 40 60 -31 31 52 -32 35 69 -33 53 52 -34 65 55 -35 63 65 -36 2 60 -37 20 20 -38 5 5 -39 60 12 -40 40 25 -41 42 7 -42 24 12 -43 23 3 -44 11 14 -45 6 38 -46 2 48 -47 8 56 -48 13 52 -49 6 68 -50 47 47 -51 49 58 -52 27 43 -53 37 31 -54 57 29 -55 63 23 -56 53 12 -57 32 12 -58 36 26 -59 21 24 -60 17 34 -61 12 24 -62 24 58 -63 27 69 -64 15 77 -65 62 77 -66 49 73 -67 67 5 -68 56 39 -69 37 47 -70 37 56 -71 57 68 -72 47 16 -73 44 17 -74 46 13 -75 49 11 -76 49 42 -77 53 43 -78 61 52 -79 57 48 -80 56 37 -81 55 54 -82 15 47 -83 14 37 -84 11 31 -85 16 22 -86 4 18 -87 28 18 -88 26 52 -89 26 35 -90 31 67 -91 15 19 -92 22 22 -93 18 24 -94 26 27 -95 25 24 -96 22 27 -97 25 21 -98 19 21 -99 20 26 -100 18 18 -101 35 35 -EOF diff --git a/src/problems/tsp/tsplib/eil51.opt.tour b/src/problems/tsp/tsplib/eil51.opt.tour deleted file mode 100644 index 4dee8b95..00000000 --- a/src/problems/tsp/tsplib/eil51.opt.tour +++ /dev/null @@ -1,59 +0,0 @@ -NAME : eil51.opt.tour -COMMENT : Optimal tour for eil51.tsp (426) -TYPE : TOUR -DIMENSION : 51 -BEST_SOLUTION: 426 -TOUR_SECTION -1 -22 -8 -26 -31 -28 -3 -36 -35 -20 -2 -29 -21 -16 -50 -34 -30 -9 -49 -10 -39 -33 -45 -15 -44 -42 -40 -19 -41 -13 -25 -14 -24 -43 -7 -23 -48 -6 -27 -51 -46 -12 -47 -18 -4 -17 -37 -5 -38 -11 -32 --1 -EOF diff --git a/src/problems/tsp/tsplib/eil51.tsp b/src/problems/tsp/tsplib/eil51.tsp deleted file mode 100644 index 543d1013..00000000 --- a/src/problems/tsp/tsplib/eil51.tsp +++ /dev/null @@ -1,58 +0,0 @@ -NAME : eil51 -COMMENT : 51-city problem (Christofides/Eilon) -TYPE : TSP -DIMENSION : 51 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 37 52 -2 49 49 -3 52 64 -4 20 26 -5 40 30 -6 21 47 -7 17 63 -8 31 62 -9 52 33 -10 51 21 -11 42 41 -12 31 32 -13 5 25 -14 12 42 -15 36 16 -16 52 41 -17 27 23 -18 17 33 -19 13 13 -20 57 58 -21 62 42 -22 42 57 -23 16 57 -24 8 52 -25 7 38 -26 27 68 -27 30 48 -28 43 67 -29 58 48 -30 58 27 -31 37 69 -32 38 46 -33 46 10 -34 61 33 -35 62 63 -36 63 69 -37 32 22 -38 45 35 -39 59 15 -40 5 6 -41 10 17 -42 21 10 -43 5 64 -44 30 15 -45 39 10 -46 32 39 -47 25 32 -48 25 55 -49 48 28 -50 56 37 -51 30 40 -EOF diff --git a/src/problems/tsp/tsplib/eil76.opt.tour b/src/problems/tsp/tsplib/eil76.opt.tour deleted file mode 100644 index 19340e20..00000000 --- a/src/problems/tsp/tsplib/eil76.opt.tour +++ /dev/null @@ -1,84 +0,0 @@ -NAME : eil76.opt.tour -COMMENT : Optimum tour for eil76.tsp (538) -TYPE : TOUR -DIMENSION : 76 -BEST_SOLUTION: 538 -TOUR_SECTION -1 -33 -63 -16 -3 -44 -32 -9 -39 -72 -58 -10 -31 -55 -25 -50 -18 -24 -49 -23 -56 -41 -43 -42 -64 -22 -61 -21 -47 -36 -69 -71 -60 -70 -20 -37 -5 -15 -57 -13 -54 -19 -14 -59 -66 -65 -38 -11 -53 -7 -35 -8 -46 -34 -52 -27 -45 -29 -48 -30 -4 -75 -76 -67 -26 -12 -40 -17 -51 -6 -68 -2 -74 -28 -62 -73 --1 -EOF diff --git a/src/problems/tsp/tsplib/eil76.tsp b/src/problems/tsp/tsplib/eil76.tsp deleted file mode 100644 index 80ae9b96..00000000 --- a/src/problems/tsp/tsplib/eil76.tsp +++ /dev/null @@ -1,83 +0,0 @@ -NAME : eil76 -COMMENT : 76-city problem (Christofides/Eilon) -TYPE : TSP -DIMENSION : 76 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 22 22 -2 36 26 -3 21 45 -4 45 35 -5 55 20 -6 33 34 -7 50 50 -8 55 45 -9 26 59 -10 40 66 -11 55 65 -12 35 51 -13 62 35 -14 62 57 -15 62 24 -16 21 36 -17 33 44 -18 9 56 -19 62 48 -20 66 14 -21 44 13 -22 26 13 -23 11 28 -24 7 43 -25 17 64 -26 41 46 -27 55 34 -28 35 16 -29 52 26 -30 43 26 -31 31 76 -32 22 53 -33 26 29 -34 50 40 -35 55 50 -36 54 10 -37 60 15 -38 47 66 -39 30 60 -40 30 50 -41 12 17 -42 15 14 -43 16 19 -44 21 48 -45 50 30 -46 51 42 -47 50 15 -48 48 21 -49 12 38 -50 15 56 -51 29 39 -52 54 38 -53 55 57 -54 67 41 -55 10 70 -56 6 25 -57 65 27 -58 40 60 -59 70 64 -60 64 4 -61 36 6 -62 30 20 -63 20 30 -64 15 5 -65 50 70 -66 57 72 -67 45 42 -68 38 33 -69 50 4 -70 66 8 -71 59 5 -72 35 60 -73 27 24 -74 40 20 -75 40 37 -76 40 40 -EOF diff --git a/src/problems/tsp/tsplib/fl1400.opt.tour b/src/problems/tsp/tsplib/fl1400.opt.tour deleted file mode 100644 index e86f0c60..00000000 --- a/src/problems/tsp/tsplib/fl1400.opt.tour +++ /dev/null @@ -1,5 +0,0 @@ -NAME : fl417 -TYPE : TOUR -DIMENSION : 417 -BEST_SOLUTION: 11861 -EOF diff --git a/src/problems/tsp/tsplib/fl1400.tsp b/src/problems/tsp/tsplib/fl1400.tsp deleted file mode 100644 index e4234ee1..00000000 --- a/src/problems/tsp/tsplib/fl1400.tsp +++ /dev/null @@ -1,1407 +0,0 @@ -NAME : fl1400 -COMMENT : Drilling problem (Reinelt) -TYPE : TSP -DIMENSION : 1400 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 2.10461e+03 1.96835e+03 -2 2.10461e+03 2.32264e+02 -3 2.95591e+02 1.96835e+03 -4 2.95591e+02 2.32264e+02 -5 1.02570e+03 1.97130e+03 -6 1.16167e+03 1.96539e+03 -7 1.19123e+03 1.95949e+03 -8 1.16759e+03 1.95949e+03 -9 1.09664e+03 1.95949e+03 -10 1.10256e+03 1.95358e+03 -11 1.09073e+03 1.95358e+03 -12 1.01979e+03 1.95358e+03 -13 1.23262e+03 1.94177e+03 -14 1.04344e+03 1.94177e+03 -15 1.23853e+03 1.93587e+03 -16 1.16759e+03 1.93587e+03 -17 1.13211e+03 1.93587e+03 -18 1.26809e+03 1.92996e+03 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a/src/problems/tsp/tsplib/fl1577.tsp b/src/problems/tsp/tsplib/fl1577.tsp deleted file mode 100644 index 299d8aa9..00000000 --- a/src/problems/tsp/tsplib/fl1577.tsp +++ /dev/null @@ -1,1584 +0,0 @@ -NAME : fl1577 -COMMENT : Drilling problem (Reinelt) -TYPE : TSP -DIMENSION : 1577 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 1.21488e+03 1.95949e+03 -2 1.22670e+03 1.95949e+03 -3 1.23853e+03 1.95949e+03 -4 1.25035e+03 1.95949e+03 -5 1.26218e+03 1.95949e+03 -6 1.28582e+03 1.95949e+03 -7 1.29765e+03 1.95949e+03 -8 1.35085e+03 1.96539e+03 -9 1.38041e+03 1.97130e+03 -10 1.39224e+03 1.97130e+03 -11 1.30356e+03 1.95358e+03 -12 1.17350e+03 1.94177e+03 -13 1.18532e+03 1.94177e+03 -14 1.19715e+03 1.94177e+03 -15 1.20897e+03 1.94177e+03 -16 1.33903e+03 1.94177e+03 -17 1.35085e+03 1.94177e+03 -18 1.36268e+03 1.94177e+03 -19 1.37450e+03 1.94177e+03 -20 1.38632e+03 1.94177e+03 -21 1.39815e+03 1.94177e+03 -22 1.26218e+03 1.93587e+03 -23 1.25035e+03 1.93587e+03 -24 1.23853e+03 1.93587e+03 -25 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a/src/problems/tsp/tsplib/fl3795.opt.tour +++ /dev/null @@ -1,6 +0,0 @@ -NAME : fl3795 -TYPE : TOUR -DIMENSION : 3795 -/* BEST_SOLUTION is lower bound from [28723,28772] */ -BEST_SOLUTION: 28723 -EOF diff --git a/src/problems/tsp/tsplib/fl3795.tsp b/src/problems/tsp/tsplib/fl3795.tsp deleted file mode 100644 index 5de4a79b..00000000 --- a/src/problems/tsp/tsplib/fl3795.tsp +++ /dev/null @@ -1,3802 +0,0 @@ -NAME : fl3795 -COMMENT : Drilling problem (Reinelt) -TYPE : TSP -DIMENSION : 3795 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 2.10461e+03 1.96835e+03 -2 1.40110e+03 1.96835e+03 -3 2.95591e+02 1.96835e+03 -4 1.23557e+03 1.95654e+03 -5 1.34790e+03 1.96244e+03 -6 1.40110e+03 1.94473e+03 -7 1.21193e+03 1.94473e+03 -8 1.21193e+03 1.93292e+03 -9 1.35381e+03 1.93292e+03 -10 1.30060e+03 1.92701e+03 -11 1.17645e+03 1.92111e+03 -12 1.21193e+03 1.92111e+03 -13 1.37746e+03 1.92111e+03 -14 1.33016e+03 1.90930e+03 -15 1.30651e+03 1.90930e+03 -16 1.23557e+03 1.90930e+03 -17 1.15281e+03 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1.80311e+03 1.49595e+02 -3788 1.81493e+03 1.49595e+02 -3789 1.82675e+03 1.49595e+02 -3790 1.83858e+03 1.49595e+02 -3791 1.85040e+03 1.49595e+02 -3792 1.86222e+03 1.49595e+02 -3793 1.87405e+03 1.49595e+02 -3794 1.88587e+03 1.49595e+02 -3795 0.00000e+00 0.00000e+00 -EOF diff --git a/src/problems/tsp/tsplib/fl417.opt.tour b/src/problems/tsp/tsplib/fl417.opt.tour deleted file mode 100644 index e86f0c60..00000000 --- a/src/problems/tsp/tsplib/fl417.opt.tour +++ /dev/null @@ -1,5 +0,0 @@ -NAME : fl417 -TYPE : TOUR -DIMENSION : 417 -BEST_SOLUTION: 11861 -EOF diff --git a/src/problems/tsp/tsplib/fl417.tsp b/src/problems/tsp/tsplib/fl417.tsp deleted file mode 100644 index ccdd4510..00000000 --- a/src/problems/tsp/tsplib/fl417.tsp +++ /dev/null @@ -1,424 +0,0 @@ -NAME : fl417 -COMMENT : Drilling problem (Reinelt) -TYPE : TSP -DIMENSION : 417 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 1.02570e+03 1.97130e+03 -2 1.16167e+03 1.96539e+03 -3 1.19123e+03 1.95949e+03 -4 1.16759e+03 1.95949e+03 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b/src/problems/tsp/tsplib/nrw1379.opt.tour deleted file mode 100644 index cd59e1db..00000000 --- a/src/problems/tsp/tsplib/nrw1379.opt.tour +++ /dev/null @@ -1,5 +0,0 @@ -NAME : nrw1379 -TYPE : TOUR -DIMENSION : 1379 -BEST_SOLUTION: 56638 -EOF diff --git a/src/problems/tsp/tsplib/nrw1379.tsp b/src/problems/tsp/tsplib/nrw1379.tsp deleted file mode 100644 index 4aa3b6b5..00000000 --- a/src/problems/tsp/tsplib/nrw1379.tsp +++ /dev/null @@ -1,1386 +0,0 @@ -NAME : nrw1379 -COMMENT : 1379 Orte in Nordrhein-Westfalen (Bachem/Wottawa) -TYPE : TSP -DIMENSION : 1379 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION - 1 2918 6528 - 2 2925 6597 - 3 2926 6609 - 4 2927 6312 - 5 2930 6328 - 6 2934 6545 - 7 2938 7412 - 8 2941 6456 - 9 2945 6284 - 10 2947 6663 - 11 2948 6513 - 12 2948 7456 - 13 2950 6350 - 14 2950 6480 - 15 2951 7293 - 16 2959 6385 - 17 2963 6476 - 18 2965 6450 - 19 2965 6602 - 20 2968 7322 - 21 2969 6212 - 22 2972 6406 - 23 2973 6671 - 24 2977 6493 - 25 2981 6576 - 26 2982 6333 - 27 2983 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a/src/problems/tsp/tsplib/usa13509.opt.tour b/src/problems/tsp/tsplib/usa13509.opt.tour deleted file mode 100644 index 74421db7..00000000 --- a/src/problems/tsp/tsplib/usa13509.opt.tour +++ /dev/null @@ -1,6 +0,0 @@ -NAME : usa13509 -TYPE : TOUR -DIMENSION : 13509 -/* BEST_SOLUTION is lower bound from [19947008,19982889] */ -BEST_SOLUTION: 19947008 -EOF diff --git a/src/problems/tsp/tsplib/usa13509.tsp b/src/problems/tsp/tsplib/usa13509.tsp deleted file mode 100644 index ae1c673a..00000000 --- a/src/problems/tsp/tsplib/usa13509.tsp +++ /dev/null @@ -1,13519 +0,0 @@ -NAME : usa13509 -COMMENT : Cities with population at least 500 in the continental US. -COMMENT : Contributed by David Applegate and Andre Rohe, based on the -COMMENT : data set "US.lat-long" from the ftp site ftp.cs.toronto.edu. -COMMENT : The file US.lat-long.Z can be found in the directory /doc/geography. -TYPE : TSP -DIMENSION : 13509 -EDGE_WEIGHT_TYPE : EUC_2D -NODE_COORD_SECTION -1 245552.778 817827.778 -2 247133.333 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a/src/state/common.rs +++ b/src/state/common.rs @@ -6,7 +6,7 @@ use serde::Serialize; use crate::{ framework::Individual, - problems::{Evaluator, MultiObjectiveProblem, Problem, SingleObjectiveProblem}, + problems::{Evaluate, MultiObjectiveProblem, Problem, SingleObjectiveProblem}, state::{CustomState, State}, }; @@ -15,16 +15,16 @@ use crate::{ /// Can be inserted manually to customize evaluation behavior. #[derive(Tid)] pub struct EvaluatorInstance<'a, P: ?Sized + 'static> { - pub(crate) evaluator: Box + 'a>, + pub(crate) evaluator: Box + 'a>, } impl<'a, P: 'static> CustomState<'a> for EvaluatorInstance<'a, P> {} -impl<'a, P: 'static> From + 'a>> for EvaluatorInstance<'a, P> { - fn from(evaluator: Box + 'a>) -> Self { +impl<'a, P: 'static> From + 'a>> for EvaluatorInstance<'a, P> { + fn from(evaluator: Box + 'a>) -> Self { EvaluatorInstance { evaluator } } } impl<'a, P: Problem> EvaluatorInstance<'a, P> { - pub fn new(evaluator: impl Evaluator + 'a) -> Self { + pub fn new(evaluator: impl Evaluate + 'a) -> Self { EvaluatorInstance { evaluator: Box::new(evaluator), } @@ -36,7 +36,7 @@ impl<'a, P: Problem> EvaluatorInstance<'a, P> { pub fn functional(evaluation: fn(&P, &mut State

, &mut [Individual

])) -> Self { struct FunctionalEvaluator(fn(&P, &mut State

, &mut [Individual

])); - impl Evaluator for FunctionalEvaluator

{ + impl Evaluate for FunctionalEvaluator

{ type Problem = P; fn evaluate( diff --git a/src/state/diversity.rs b/src/state/diversity.rs index 6889e8a2..b9832c05 100644 --- a/src/state/diversity.rs +++ b/src/state/diversity.rs @@ -61,12 +61,12 @@ where #[derive(serde::Serialize, Clone, Default)] pub struct DimensionWiseDiversity; impl DimensionWiseDiversity { - pub fn new> + VectorProblem>() -> Box> - { + pub fn new> + VectorProblem>( + ) -> Box> { Box::new(DiversityMeasurement(Self)) } } -impl> + VectorProblem> DiversityMeasure

+impl> + VectorProblem> DiversityMeasure

for DimensionWiseDiversity { fn measure(&self, problem: &P, solutions: &[&Vec]) -> f64 { @@ -86,12 +86,12 @@ impl> + VectorProblem> DiversityMeasure< #[derive(serde::Serialize, Clone, Default)] pub struct PairwiseDistanceDiversity; impl PairwiseDistanceDiversity { - pub fn new> + VectorProblem>() -> Box> - { + pub fn new> + VectorProblem>( + ) -> Box> { Box::new(DiversityMeasurement(Self)) } } -impl> + VectorProblem> DiversityMeasure

+impl> + VectorProblem> DiversityMeasure

for PairwiseDistanceDiversity { fn measure(&self, problem: &P, solutions: &[&Vec]) -> f64 { @@ -104,7 +104,6 @@ impl> + VectorProblem> DiversityMeasure< for i in 1..n as usize { for j in 0..=i - 1 { sum += (0..d) - .into_iter() .map(|k| (solutions[i][k] - solutions[j][k]).powi(2)) .sum::(); diversity += sum.sqrt(); @@ -119,12 +118,12 @@ impl> + VectorProblem> DiversityMeasure< #[derive(serde::Serialize, Clone, Default)] pub struct TrueDiversity; impl TrueDiversity { - pub fn new> + VectorProblem>() -> Box> - { + pub fn new> + VectorProblem>( + ) -> Box> { Box::new(DiversityMeasurement(Self)) } } -impl> + VectorProblem> DiversityMeasure

+impl> + VectorProblem> DiversityMeasure

for TrueDiversity { fn measure(&self, problem: &P, solutions: &[&Vec]) -> f64 { @@ -146,12 +145,12 @@ impl> + VectorProblem> DiversityMeasure< #[derive(serde::Serialize, Clone, Default)] pub struct DistanceToAveragePointDiversity; impl DistanceToAveragePointDiversity { - pub fn new> + VectorProblem>() -> Box> - { + pub fn new> + VectorProblem>( + ) -> Box> { Box::new(DiversityMeasurement(Self)) } } -impl> + VectorProblem> DiversityMeasure

+impl> + VectorProblem> DiversityMeasure

for DistanceToAveragePointDiversity { fn measure(&self, problem: &P, solutions: &[&Vec]) -> f64 { @@ -162,7 +161,6 @@ impl> + VectorProblem> DiversityMeasure< for i in solutions { sum += (0..d) - .into_iter() .map(|k| { let xk = solutions.iter().map(|s| s[k]).sum::() / n; (i[k] - xk).powi(2) diff --git a/src/state/pso.rs b/src/state/pso.rs index 25b76625..fec14ea1 100644 --- a/src/state/pso.rs +++ b/src/state/pso.rs @@ -22,7 +22,7 @@ impl CustomState<'_> for PsoState

{} impl PsoState

where - P: SingleObjectiveProblem> + LimitedVectorProblem, + P: SingleObjectiveProblem> + LimitedVectorProblem, { /// State initialization for PSO. /// @@ -35,7 +35,7 @@ where impl

Component

for PsoStateInitialization where - P: SingleObjectiveProblem> + LimitedVectorProblem, + P: SingleObjectiveProblem> + LimitedVectorProblem, { fn initialize(&self, _problem: &P, state: &mut State

) { // Initialize with empty state to satisfy `state.require()` statements @@ -88,7 +88,7 @@ where impl

Component

for PsoStateUpdate where - P: Problem> + LimitedVectorProblem, + P: Problem> + LimitedVectorProblem, { fn initialize(&self, _problem: &P, state: &mut State

) { state.require::>(); diff --git a/src/testing.rs b/src/testing.rs index 11028d3e..849544ba 100644 --- a/src/testing.rs +++ b/src/testing.rs @@ -1,9 +1,11 @@ //! Testing utilities. +#![allow(dead_code)] + use crate::{ framework::{Individual, SingleObjective}, - problems::{Evaluator, HasKnownOptimum, Problem, SingleObjectiveProblem}, - state::{common::EvaluatorInstance, State}, + problems::{Evaluate, KnownOptimumProblem, Problem, SingleObjectiveProblem}, + state::State, }; use std::borrow::Borrow; @@ -17,21 +19,18 @@ impl Problem for TestProblem { fn name(&self) -> &str { "TestProblem" } - - fn default_evaluator<'a>(&self) -> EvaluatorInstance<'a, Self> { - EvaluatorInstance::new(TestEvaluator) - } } -impl HasKnownOptimum for TestProblem { +impl KnownOptimumProblem for TestProblem { fn known_optimum(&self) -> SingleObjective { 0.0.try_into().unwrap() } } +#[derive(Default)] pub struct TestEvaluator; -impl Evaluator for TestEvaluator { +impl Evaluate for TestEvaluator { type Problem = TestProblem; fn evaluate( @@ -41,7 +40,7 @@ impl Evaluator for TestEvaluator { individuals: &mut [Individual], ) { for individual in individuals { - individual.evaluate(0.0.try_into().unwrap()); + individual.set_objective(0.0.try_into().unwrap()); } } } diff --git a/src/tracking/files.rs b/src/tracking/files.rs index cc640a79..b9a84099 100644 --- a/src/tracking/files.rs +++ b/src/tracking/files.rs @@ -5,8 +5,8 @@ use std::{ path::Path, }; -use anyhow::Context; use erased_serde::Serialize as DynSerialize; +use eyre::WrapErr; use serde::Serialize; use crate::tracking::Log; @@ -58,8 +58,8 @@ pub fn write_log( } /// Write the [Log] to a file. -pub fn write_log_file(output: impl AsRef, log: &Log) -> anyhow::Result<()> { - let file = File::create(output.as_ref()).context("failed to create log file")?; +pub fn write_log_file(output: impl AsRef, log: &Log) -> eyre::Result<()> { + let file = File::create(output.as_ref()).wrap_err("failed to create log file")?; let writer = &mut BufWriter::new(file); write_log(writer, log).context("failed to serialize log") } diff --git a/src/utils.rs b/src/utils.rs new file mode 100644 index 00000000..5ed3e6c3 --- /dev/null +++ b/src/utils.rs @@ -0,0 +1,104 @@ +//! A collection of utilities. + +/// Allows enumeration for functions which normally don't support enumeration, e.g. [`Vec::retain`]. +/// +/// # Examples +/// +/// ``` +/// use mahf::utils::with_index; +/// +/// let mut v = vec![1, 2, 3, 4, 5]; +/// // Remove every second element. +/// v.retain(with_index(|index, _value| index % 2 == 0)); +/// assert_eq!(v, vec![1, 3, 5]); +/// ``` +pub fn with_index(mut f: F) -> impl FnMut(&T) -> bool +where + F: FnMut(usize, &T) -> bool, +{ + let mut i = 0; + move |item| (f(i, item), i += 1).0 +} + +/// A trait for giving a type a useful default value that may fail in a controlled +/// way under some circumstances. +/// +/// This is useful for handling types with a default implementation the same way like types that don't. +/// +/// This trait is automatically implemented for types that implement [Default]. +/// A failing implementation can be generated using the [`impl_try_default_err!()`] macro. +/// +/// [`impl_try_default_err!()`]: impl_try_default_err +/// +/// # Examples +/// +/// Manual implementation: +/// +/// ``` +/// use mahf::utils::TryDefault; +/// +/// pub struct TypeThatNeedsManualInitialization; +/// +/// impl TryDefault for TypeThatNeedsManualInitialization { +/// type Error = (); +/// +/// fn try_default() -> Result { +/// Err(()) +/// } +/// } +/// +/// ``` +/// +/// Using the [`impl_try_default_err!()`] macro generates the same code: +/// +/// ``` +/// use mahf::utils::{TryDefault, impl_try_default_err}; +/// +/// pub struct TypeThatNeedsManualInitialization; +/// +/// impl_try_default_err!(TypeThatNeedsManualInitialization); +/// +/// ``` +pub trait TryDefault: Sized { + type Error; + + /// Returns the "default value" for a type. + /// Default values are often some kind of initial value, identity value, or anything else that may make sense as a default. + /// + /// # Examples + /// + /// ``` + /// use mahf::utils::TryDefault; + /// assert_eq!(Ok(0), i8::try_default()); + /// assert_eq!(Ok(0.0), f64::try_default()); + /// ``` + fn try_default() -> Result; +} + +impl TryDefault for T { + type Error = (); + + fn try_default() -> Result { + Ok(T::default()) + } +} + +mod macros { + /// Default implementation for returning `Err` for [`TryDefault`]. + #[macro_export] + macro_rules! impl_try_default_err { + ($ty:ty) => { + impl TryDefault for $ty { + type Error = (); + + fn try_default() -> Result { + Err(()) + } + } + }; + } + + pub use impl_try_default_err; +} + +pub use macros::impl_try_default_err; diff --git a/src/utils/mod.rs b/src/utils/mod.rs deleted file mode 100644 index 95f62d27..00000000 --- a/src/utils/mod.rs +++ /dev/null @@ -1,4 +0,0 @@ -//! Utils supporting efficient evaluation of heuristics. - -pub mod prompt; -pub mod threads; diff --git a/src/utils/prompt.rs b/src/utils/prompt.rs deleted file mode 100644 index 127fe7a3..00000000 --- a/src/utils/prompt.rs +++ /dev/null @@ -1,30 +0,0 @@ -//! CLI prompt utilities. - -use std::{fs, io, path::Path}; - -pub fn data_dir(data_dir: &str) -> io::Result> { - if Path::new(data_dir).exists() { - println!("There already exists data from a previous run."); - println!("Current path: {}", data_dir); - println!("Options: y -> delete existing data"); - println!(" r -> rename data directory"); - println!(" n -> cancel execution"); - let reply = rprompt::prompt_reply_stdout("(Y/r/n) ")?; - - #[allow(clippy::wildcard_in_or_patterns)] - match reply.as_str() { - "" | "y" | "Y" => { - fs::remove_dir_all(data_dir)?; - println!("Old data has been removed."); - Ok(Some(data_dir.to_owned())) - } - "r" | "R" => { - let reply = rprompt::prompt_reply_stdout("New data name: ")?; - Ok(Some(reply)) - } - "n" | "N" | _ => Ok(None), - } - } else { - Ok(Some(data_dir.to_owned())) - } -} diff --git a/src/utils/threads.rs b/src/utils/threads.rs deleted file mode 100644 index daac7a17..00000000 --- a/src/utils/threads.rs +++ /dev/null @@ -1,64 +0,0 @@ -//! Helpers for multi threading. - -use std::{sync::mpsc, thread}; - -pub use num_cpus; - -type Task = Box; - -/// A synchronous (blocking) thread pool. -/// -/// This allows executing a specific number of tasks in parallel, -/// but [`SyncThreadPool::enqueue`] will block when all available workers are busy. -pub struct SyncThreadPool { - threads: Vec>, - pool: mpsc::Receiver>, -} - -impl Default for SyncThreadPool { - /// This is equivalent to `SyncThreadPool::new(num_cpus::get())`. - fn default() -> Self { - Self::new(num_cpus::get()) - } -} - -impl SyncThreadPool { - /// Creates a sync thread pool with a custom number of workers. - /// - /// Use [`SyncThreadPool::default`] to create a thread poll with - /// one worker per processor thread. - pub fn new(workers: usize) -> Self { - let (sender, receiver) = mpsc::channel(); - let mut pool = SyncThreadPool { - threads: Vec::with_capacity(workers), - pool: receiver, - }; - - for _ in 0..workers { - let pool_sender = sender.clone(); - pool.threads.push(thread::spawn(move || { - let (tx, rx) = mpsc::channel(); - if pool_sender.send(tx.clone()).is_err() { - return; - } - while let Ok(task) = rx.recv() { - task(); - if pool_sender.send(tx.clone()).is_err() { - return; - }; - } - })); - } - - pool - } - - /// Enqueues a new task. - /// - /// This will be non-blocking until all workers are occupied. - /// Once that is the case, it will block until a worker becomes - /// available again. - pub fn enqueue(&mut self, task: impl FnOnce() + Send + 'static) { - self.pool.recv().unwrap().send(Box::new(task)).unwrap(); - } -}