@@ -2231,14 +2231,22 @@ def best_solution(self, pop_fitness=None):
22312231 # break ties using the second objective, then third, etc.
22322232 pop_fitness_arr = numpy .array (pop_fitness )
22332233 # Get the indices that would sort by all objectives in descending order
2234- sorted_indices = numpy .lexsort ([ - pop_fitness_arr [:,i ] for i in reversed (range (pop_fitness_arr .shape [1 ])) ])
2235- best_match_idx = sorted_indices [0 ]
2236- maximum_fitness_value = pop_fitness_arr [best_match_idx ]
2237- 2238- best_match_list = numpy .where (
2239- pop_fitness == maximum_fitness_value )
2234+ if pop_fitness_arr .ndim == 1 :
2235+ # Single-objective optimization.
2236+ best_match_idx = numpy .where (
2237+ pop_fitness == numpy .max (pop_fitness ))[0 ][0 ]
2238+ elif pop_fitness_arr .ndim == 2 :
2239+ # Multi-objective optimization.
2240+ # Sort by all objectives in descending order.
2241+ # The first objective is the most important, then the second, etc.
2242+ sorted_indices = numpy .lexsort ([ - pop_fitness_arr [:,i ] for i in reversed (range (pop_fitness_arr .shape [1 ])) ])
2243+ best_match_idx = sorted_indices [0 ]
2244+ maximum_fitness_value = pop_fitness_arr [best_match_idx ]
2245+ 2246+ best_match_list = numpy .where (
2247+ pop_fitness == maximum_fitness_value )
22402248
2241- best_match_idx = best_match_list [0 ][0 ] # Get the first index of the best match.
2249+ best_match_idx = best_match_list [0 ][0 ] # Get the first index of the best match.
22422250
22432251
22442252 best_solution = self .population [best_match_idx , :].copy ()
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