From 7e068ddd25edb18ded0011d4a070d870644678ec Mon Sep 17 00:00:00 2001
From: Ruslan Shaiakhmetov <42977731+Crylab@users.noreply.github.com>
Date: Tue, 8 Jul 2025 01:42:10 +0200
Subject: [PATCH 1/2] fix: best_solution for multi-objective optimization

This fix allows to optimize both single-objective and multi-objective optimization cases
---
 pygad/pygad.py | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/pygad/pygad.py b/pygad/pygad.py
index 3661dab..d0413ad 100644
--- a/pygad/pygad.py
+++ b/pygad/pygad.py
@@ -2227,8 +2227,19 @@ def best_solution(self, pop_fitness=None):
                 raise ValueError(f"The type of the 'pop_fitness' parameter is expected to be list, tuple, or numpy.ndarray but ({type(pop_fitness)}) found.")
 
             # Return the index of the best solution that has the best fitness value.
-            best_match_idx = numpy.where(
-                pop_fitness == numpy.max(pop_fitness))[0][0]
+            # For multi-objective optimization: find the index of the solution with the maximum fitness in the first objective,
+            # break ties using the second objective, then third, etc.
+            pop_fitness_arr = numpy.array(pop_fitness)
+            # Get the indices that would sort by all objectives in descending order
+            sorted_indices = numpy.lexsort([ -pop_fitness_arr[:,i] for i in reversed(range(pop_fitness_arr.shape[1])) ])
+            best_match_idx = sorted_indices[0]
+            maximum_fitness_value = pop_fitness_arr[best_match_idx]
+
+            best_match_list = numpy.where(
+                pop_fitness == maximum_fitness_value)
+            
+            best_match_idx = best_match_list[0][0]  # Get the first index of the best match.
+
 
             best_solution = self.population[best_match_idx, :].copy()
             best_solution_fitness = pop_fitness[best_match_idx]
@@ -2494,4 +2505,4 @@ def load(filename):
     except:
         # raise BaseException("Error loading the file. If the file already exists, please reload all the functions previously used (e.g. fitness function).")
         raise BaseException("Error loading the file.")
-    return ga_in
\ No newline at end of file
+    return ga_in

From b2ac512b9de5f6516c664e38f13ee41e80d6bdf2 Mon Sep 17 00:00:00 2001
From: Ruslan Shaiakhmetov <42977731+Crylab@users.noreply.github.com>
Date: Tue, 8 Jul 2025 12:22:20 +0200
Subject: [PATCH 2/2] fix: keep original code + fix for multi-objective
 optimization

---
 pygad/pygad.py | 22 +++++++++++++++-------
 1 file changed, 15 insertions(+), 7 deletions(-)

diff --git a/pygad/pygad.py b/pygad/pygad.py
index d0413ad..66e4b31 100644
--- a/pygad/pygad.py
+++ b/pygad/pygad.py
@@ -2231,14 +2231,22 @@ def best_solution(self, pop_fitness=None):
             # break ties using the second objective, then third, etc.
             pop_fitness_arr = numpy.array(pop_fitness)
             # Get the indices that would sort by all objectives in descending order
-            sorted_indices = numpy.lexsort([ -pop_fitness_arr[:,i] for i in reversed(range(pop_fitness_arr.shape[1])) ])
-            best_match_idx = sorted_indices[0]
-            maximum_fitness_value = pop_fitness_arr[best_match_idx]
-
-            best_match_list = numpy.where(
-                pop_fitness == maximum_fitness_value)
+            if pop_fitness_arr.ndim == 1:
+                # Single-objective optimization.
+                best_match_idx = numpy.where(
+                 pop_fitness == numpy.max(pop_fitness))[0][0]
+            elif pop_fitness_arr.ndim == 2:
+                # Multi-objective optimization.
+                # Sort by all objectives in descending order.
+                # The first objective is the most important, then the second, etc.
+                sorted_indices = numpy.lexsort([ -pop_fitness_arr[:,i] for i in reversed(range(pop_fitness_arr.shape[1])) ])
+                best_match_idx = sorted_indices[0]
+                maximum_fitness_value = pop_fitness_arr[best_match_idx]
+
+                best_match_list = numpy.where(
+                    pop_fitness == maximum_fitness_value)
             
-            best_match_idx = best_match_list[0][0]  # Get the first index of the best match.
+                best_match_idx = best_match_list[0][0]  # Get the first index of the best match.
 
 
             best_solution = self.population[best_match_idx, :].copy()