From 112f40d00f45a479cdb41b569e6b2972950c8441 Mon Sep 17 00:00:00 2001
From: coolneng <akasroua@gmail.com>
Date: Mon, 21 Jun 2021 21:41:00 +0200
Subject: [PATCH] Replace the population after the local search
---
src/memetic_algorithm.py | 29 +++++++++++++++++++++--------
1 file changed, 21 insertions(+), 8 deletions(-)
diff --git a/src/memetic_algorithm.py b/src/memetic_algorithm.py
index 2f25fcc..9a07df0 100644
--- a/src/memetic_algorithm.py
+++ b/src/memetic_algorithm.py
@@ -13,21 +13,34 @@ def get_best_indices(n, population):
return best_elements
+def replace_elements(current_population, new_population, indices):
+ for item in indices:
+ current_population[item] = new_population[item]
+ return current_population
+
+
def run_local_search(n, data, population, mode, probability=0.1):
- new_population = []
+ neighbourhood = []
if mode == "all":
for individual in population:
- new_population.append(local_search(individual, n, data))
+ neighbourhood.append(local_search(individual, n, data))
+ new_population = neighbourhood
elif mode == "random":
expected_individuals = len(population) * probability
+ indices = []
for _ in range(expected_individuals):
- random_individual = population[randint(len(population))]
- new_population.append(local_search(random_individual, n, data))
+ random_index = randint(len(population))
+ random_individual = population[random_index]
+ neighbourhood.append(local_search(random_individual, n, data))
+ indices.append(random_index)
+ new_population = replace_elements(population, neighbourhood, indices)
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))
+ best_indices = get_best_indices(n=expected_individuals, population=population)
+ for element in best_indices:
+ neighbourhood.append(local_search(population[element], n, data))
+ new_population = replace_elements(population, neighbourhood, best_indices)
+ return new_population
def memetic_algorithm(n, m, data, hybridation, max_iterations=100000):
@@ -36,7 +49,7 @@ 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, data, population, mode=hybridation)
+ population = run_local_search(n, data, population, mode=hybridation)
i += 5
parents = select_parents(population, n, mode="stationary")
offspring = crossover(mode="position", parents=parents, m=m)