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Add memetic algorithm prototype

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coolneng 2021-06-21 07:39:51 +02:00
parent ab4748d28e
commit 4e640ffc2d
Signed by: coolneng
GPG Key ID: 9893DA236405AF57
1 changed files with 18 additions and 46 deletions
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src/memetic_algorithm.py
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@ -1,50 +1,22 @@
from numpy.random import choice, seed from genetic_algorithm import *
from local_search import local_search
def get_first_random_solution(m, data): def run_local_search(n, m, data, individual):
seed(42) pass
random_indexes = choice(len(data.index), size=m, replace=False)
return data.loc[random_indexes]
def element_in_dataframe(solution, element): def memetic_algorithm(n, m, data, hybridation, max_iterations=100000):
duplicates = solution.query( population = [generate_individual(n, m, data) for _ in range(n)]
f"(source == {element.source} and destination == {element.destination}) or (source == {element.destination} and destination == {element.source})" population = evaluate_population(population, data)
) for i in range(max_iterations):
return not duplicates.empty if i % 10 == 0:
best_index, _ = get_best_elements(population)
run_local_search(n, m, data, individual=population[best_index])
def replace_worst_element(previous, data): parents = select_parents(population, n, mode="stationary")
solution = previous.copy() offspring = crossover(mode="uniform", parents=parents, m=m)
worst_index = solution["distance"].astype(float).idxmin() offspring = mutate(offspring, n, data)
random_element = data.sample().squeeze() population = replace_population(population, offspring, mode="stationary")
while element_in_dataframe(solution=solution, element=random_element): population = evaluate_population(population, data)
random_element = data.sample().squeeze() best_index, _ = get_best_elements(population)
solution.loc[worst_index] = random_element return population[best_index]
return solution, worst_index
def get_random_solution(previous, data):
solution, worst_index = replace_worst_element(previous, data)
previous_worst_distance = previous["distance"].loc[worst_index]
while solution.distance.loc[worst_index] <= previous_worst_distance:
solution, _ = replace_worst_element(previous=solution, data=data)
return solution
def explore_neighbourhood(element, data, max_iterations=100000):
neighbourhood = []
neighbourhood.append(element)
for _ in range(max_iterations):
previous_solution = neighbourhood[-1]
neighbour = get_random_solution(previous=previous_solution, data=data)
neighbourhood.append(neighbour)
return neighbour
def memetic_algorithm(m, data):
first_solution = get_first_random_solution(m=m, data=data)
best_solution = explore_neighbourhood(
element=first_solution, data=data, max_iterations=100
)
return best_solution