Nature-inspired algorithms are a very popular tool for solving optimization problems. Numerous variants of nature-inspired algorithms have been developed since the beginning of their era. To prove their versatility, those were tested in various domains on various applications, especially when they are hybridized, modified or adapted. However, implementation of nature-inspired algorithms is sometimes a difficult, complex and tedious task. In order to break this wall, NiaPy is intended for simple and quick use, without spending time for implementing algorithms from scratch.
- numerous benchmark functions implementations,
- use of various nature-inspired algorithms without struggle and effort with a simple interface,
- easy comparison between nature-inspired algorithms and
- export of results in various formats (LaTeX, JSON, Excel).
Python micro framework for building nature-inspired algorithms. Official documentation is available here.
The micro framework features following algorithms:
- basic:
- Artificial bee colony algorithm
- Bat algorithm
- Camel algorithm
- Cuckoo search
- Differential evolution algorithm
- Evolution Strategy
- Firefly algorithm
- Fireworks algorithm
- Flower pollination algorithm
- Forest optimization algorithm
- Genetic algorithm
- Glowworm swarm optimization
- Grey wolf optimizer
- Monarch butterfly optimization
- Moth flame optimizer
- Harmony Search algorithm
- Krill herd algorithm
- Monkey king evolution
- Multiple trajectory search
- Particle swarm optimization
- Sine cosine algorithm
- modified:
- Hybrid bat algorithm
- Self-adaptive differential evolution algorithm
- Dynamic population size self-adaptive differential evolution algorithm
- other:
- Anarchic society optimization algorithm
- Hill climbing algorithm
- Multiple trajectory search
- Nelder mead method or downhill simplex method or amoeba method
- Simulated annealing algorithm
The following benchmark functions are included in NiaPy:
- Ackley
- Alpine
- Alpine1
- Alpine2
- Bent Cigar
- Chung Reynolds
- Csendes
- Discus
- Dixon-Price
- Elliptic
- Griewank
- Happy cat
- HGBat
- Katsuura
- Levy
- Michalewicz
- Perm
- Pintér
- Powell
- Qing
- Quintic
- Rastrigin
- Ridge
- Rosenbrock
- Salomon
- Schumer Steiglitz
- Schwefel
- Schwefel 2.21
- Schwefel 2.22
- Sphere
- Sphere2 -> Sphere with different powers
- Sphere3 -> Rotated hyper-ellipsoid
- Step
- Step2
- Step3
- Stepint
- Styblinski-Tang
- Sum Squares
- Trid
- Weierstrass
- Whitley
- Zakharov
- Python 3.6.x or 3.7.x (backward compatibility with 2.7.x)
- Pip
- numpy >= 1.16.2
- scipy >= 1.2.1
- enum34 >= 1.1.6 (if using python version < 3.4)
- xlsxwriter >= 1.1.6
- matplotlib >= 2.2.4
List of development dependencies and requirements can be found in the installation section of NiaPy documentation.
Install NiaPy with pip:
$ pip install NiaPyInstall NiaPy with conda:
$ conda install -c niaorg niapyOr directly from the source code:
$ git clone https://github.com/NiaOrg/NiaPy.git
$ cd NiaPy
$ python setup.py installAfter installation, the package can imported:
$ python
>>> import NiaPy
>>> NiaPy.__version__For more usage examples please look at examples folder.
We encourage you to contribute to NiaPy! Please check out the Contributing to NiaPy guide for guidelines about how to proceed.
Everyone interacting in NiaPy's codebases, issue trackers, chat rooms and mailing lists is expected to follow the NiaPy code of conduct.
This package is distributed under the MIT License. This license can be found online at http://www.opensource.org/licenses/MIT.
This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!
- fixes and improvements of algorithm interface
- various algorithm implementation fixes
- add support for Python 3.7
- documentation fixes
- introduced maximization
- added algorithms: - Fish School Search - Coral Reefs Optimization - Cuckoo Search - Forest Optimization Algorithm - Monarch Butterfly Optimization
- update runner utility
- update examples
- various bugfixes
- fix dependecies versions
- added moth flame optimizer
- added new examples
- documentation updates
- PSO and BBFWA algorithms fixes
- stopping conditions fixes
- added new test cases
- added multiple seed option
- various bugfixes
- fix PyPI build
Changes included in release:
- Added algorithms:
- basic:
- Camel algorithm
- Evolution Strategy
- Fireworks algorithm
- Glowworm swarm optimization
- Harmony search algorithm
- Krill Herd Algorithm
- Monkey King Evolution
- Multiple trajectory search
- Sine Cosine Algorithm
- modified:
- Dynamic population size self-adaptive differential evolution algorithm
- other:
- Anarchic society optimization algorithm
- Hill climbing algorithm
- Multiple trajectory search
- Nelder mead method or downhill simplex method or amoeba method
- Simulated annealing algorithm
- Added benchmarks functions:
- Discus
- Dixon-Price
- Elliptic
- HGBat
- Katsuura
- Levy
- Michalewicz
- Perm
- Powell
- Sphere2 -> Sphere with different powers
- Sphere3 -> Rotated hyper-ellipsoid
- Trid
- Weierstrass
- Zakharov
- breaking changes in algorithms structure
- various bugfixes
- fix Bat and Hybrid Bat algorithms
This release reflects the changes from Journal of Open Source Software (JOSS) review: - Better API Documentation - Clarification of set-up requirements in README - Improved paper
- stable release 1.0.0
- fix PyPI build
- version 1.0.0 release candidate 1
- added 10 algorithms
- added 26 benchmark functions
- added Runner utility with export functionality