60 lines
2.4 KiB
Python
60 lines
2.4 KiB
Python
from genetic_algorithm import *
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from local_search import local_search
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from copy import deepcopy
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def get_best_indices(n, population):
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select_population = deepcopy(population)
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best_elements = []
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for _ in range(n):
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best_index, _ = get_best_elements(select_population)
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best_elements.append(best_index)
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select_population.pop(best_index)
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return best_elements
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def replace_elements(current_population, new_population, indices):
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for item in indices:
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current_population[item] = new_population[item]
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return current_population
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def run_local_search(n, data, population, mode, probability=0.1):
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neighbourhood = []
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if mode == "all":
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for individual in population:
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neighbourhood.append(local_search(individual, n, data))
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new_population = neighbourhood
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elif mode == "random":
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expected_individuals = len(population) * probability
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indices = []
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for _ in range(expected_individuals):
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random_index = randint(len(population))
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random_individual = population[random_index]
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neighbourhood.append(local_search(random_individual, n, data))
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indices.append(random_index)
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new_population = replace_elements(population, neighbourhood, indices)
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else:
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expected_individuals = len(population) * probability
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best_indices = get_best_indices(n=expected_individuals, population=population)
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for element in best_indices:
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neighbourhood.append(local_search(population[element], n, data))
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new_population = replace_elements(population, neighbourhood, best_indices)
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return new_population
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def memetic_algorithm(n, m, data, hybridation, max_iterations=100000):
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population = [generate_individual(n, m, data) for _ in range(n)]
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population = evaluate_population(population, data)
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for i in range(max_iterations):
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if i % 10 == 0:
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population = run_local_search(n, data, population, mode=hybridation)
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i += 5
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parents = select_parents(population, n, mode="stationary")
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offspring = crossover(mode="position", parents=parents, m=m)
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offspring = mutate(offspring, n, data)
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population = replace_population(population, offspring, mode="stationary")
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population = evaluate_population(population, data)
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best_index, _ = get_best_elements(population)
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return population[best_index]
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