From f61cb7002e8b149b12866e6aac8a3f2fc7408cca Mon Sep 17 00:00:00 2001 From: coolneng Date: Mon, 21 Jun 2021 18:22:31 +0200 Subject: [PATCH] Implement memetic algorithm --- src/local_search.py | 5 ++--- src/memetic_algorithm.py | 32 ++++++++++++++++++++++++++++---- 2 files changed, 30 insertions(+), 7 deletions(-) diff --git a/src/local_search.py b/src/local_search.py index 06f6834..79a6bfc 100644 --- a/src/local_search.py +++ b/src/local_search.py @@ -67,9 +67,8 @@ def explore_neighbourhood(element, n, data, max_iterations=100000): return neighbour -def local_search(n, m, data): - first_solution = get_first_random_solution(n, m, data) +def local_search(first_solution, n, data): best_solution = explore_neighbourhood( - element=first_solution, n=n, data=data, max_iterations=100 + element=first_solution, n=n, data=data, max_iterations=50 ) return best_solution diff --git a/src/memetic_algorithm.py b/src/memetic_algorithm.py index 20383c7..0308150 100644 --- a/src/memetic_algorithm.py +++ b/src/memetic_algorithm.py @@ -1,9 +1,33 @@ from genetic_algorithm import * from local_search import local_search +from copy import deepcopy -def run_local_search(n, m, data, individual): - pass +def get_best_indices(n, population): + select_population = deepcopy(population) + best_elements = [] + for _ in range(n): + best_index, _ = get_best_elements(select_population) + best_elements.append(best_index) + select_population.pop(best_index) + return best_elements + + +def run_local_search(n, data, population, mode, probability=0.1): + new_population = [] + if mode == "all": + for individual in population: + new_population.append(local_search(individual, n, data)) + elif mode == "random": + expected_individuals = len(population) * probability + for _ in range(expected_individuals): + random_individual = population[randint(len(population))] + new_population.append(local_search(random_individual, n, data)) + else: + expected_individuals = len(population) * probability + best_indexes = get_best_indices(n=expected_individuals, population=population) + for element in best_indexes: + new_population.append(local_search(population[element], n, data)) def memetic_algorithm(n, m, data, hybridation, max_iterations=100000): @@ -12,9 +36,9 @@ def memetic_algorithm(n, m, data, hybridation, max_iterations=100000): for i in range(max_iterations): if i % 10 == 0: best_index, _ = get_best_elements(population) - run_local_search(n, m, data, individual=population[best_index]) + run_local_search(n, data, population, mode=hybridation) parents = select_parents(population, n, mode="stationary") - offspring = crossover(mode="uniform", parents=parents, m=m) + offspring = crossover(mode="position", parents=parents, m=m) offspring = mutate(offspring, n, data) population = replace_population(population, offspring, mode="stationary") population = evaluate_population(population, data)