for _ in range(10000):
population.next_generation()
# resultado final
final_population = population.get_population()
best_solution = final_population[0]
best_chromosome = best_solution[0]
best_distance = best_solution[1]
print("Tamanho da população: {}".format(len(final_population)))
print("Taxa de mutação: 5%")
print("Número de cidades: {}".format(cities_location.get_cities_count()))
print("Melhor solução: {}".format(best_distance))
plt.plot(cities_location.get_cities()[0], cities_location.get_cities()[1], 'ro')
best_chromosome_cities = best_chromosome.get_cities()
best_path_x = []
best_path_y = []
for i in range(len(best_chromosome_cities)):
city = cities_location.get_city(best_chromosome_cities[i])
best_path_x.append(city.x)
best_path_y.append(city.y)
first_city = cities_location.get_city(best_chromosome_cities[0])
best_path_x.append(first_city.x)
best_path_y.append(first_city.y)
plt.plot(best_path_x, best_path_y)
def handler(event, context):
body_cities = None
body_distances = None
population_size = 20
mutation_rate = 1 # 1% - taxa de mutação
generations = 1000 # critério de parada
time_distances = []
if event:
event = json.loads(event['body'])
population_size = int(event['populationSize'])
mutation_rate = int(event["mutationRate"])
generations = int(event["generations"])
body_cities = event['cities']
body_distances = event["distances"]
try:
c = Cities()
if body_cities and body_distances:
c.set_cities(body_cities, body_distances)
else:
c.test() # carrega cidades para testes
cities_list = c.get_cities()
for city in cities_list:
print("Distâncias da cidade: %s\n******" % city.name)
time_distances.append(city.distances)
print(city.distances)
for index, distance in enumerate(city.distances):
print("De %s --> %s = %s" %
(city.name, cities_list[index].name, distance))
ga = GeneticAlgorithm(population_size)
result = ga.resolve(mutation_rate, generations, time_distances,
cities_list)
print({
'generation': result[0],
'travelled_distance': result[1],
'chromosome': result[2],
'cities': c.chromose_to_cities(result[2])
})
return {
'statusCode':
200,
'headers': {
'Access-Control-Allow-Origin': '*',
'Content-Type': 'application/json'
},
'body':
json.dumps({
'generation': result[0],
'travelled_distance': result[1],
'chromosome': result[2],
'cities': c.chromose_to_cities(result[2])
})
}
except ImportError:
print(ImportError)
return {
'statusCode': 500,
'body': json.dumps({'message': "Erro ao executar"})
}
population.next_generation()
# resultado final
final_population = population.get_population()
best_solution = final_population[0]
best_chromosome = best_solution[0]
best_distance = best_solution[1]
print("Tamanho da população: {}".format(len(final_population)))
print("Taxa de mutação: 10%")
print("Número de cidades: {}".format(cities_location.get_cities_count()))
print("Melhor custo: {}".format(best_distance))
print("Melhor solução: {}".format(best_chromosome.get_cities()))
# Exibe os dados no matplot
plt.plot(cities_location.get_cities()[0],
cities_location.get_cities()[1], 'ro')
best_chromosome_cities = best_chromosome.get_cities()
best_path_x = []
best_path_y = []
for i in range(len(best_chromosome_cities)):
city = cities_location.get_city(best_chromosome_cities[i])
best_path_x.append(city.x)
best_path_y.append(city.y)
first_city = cities_location.get_city(best_chromosome_cities[0])
best_path_x.append(first_city.x)
best_path_y.append(first_city.y)
plt.plot(best_path_x, best_path_y)