遗传算法解决多配送中心vrp问题 python代码

以下是使用遗传算法解决多配送中心VRP问题的Python代码示例：

import numpy as np
import random

# 距离矩阵
distance_matrix = np.array([
    [0, 3, 1, 5, 8, 6],
    [3, 0, 2, 4, 6, 7],
    [1, 2, 0, 6, 9, 4],
    [5, 4, 6, 0, 3, 1],
    [8, 6, 9, 3, 0, 2],
    [6, 7, 4, 1, 2, 0]
])

# 需求量
demand = np.array([0, 2, 4, 6, 3, 5])

# 车辆容量限制
capacity = 10

# 遗传算法参数
population_size = 50
elite_size = 10
mutation_rate = 0.01
generations = 100

# 随机生成初始种群
def initial_population(population_size, demand):
    population = []
    for i in range(population_size):
        chromosome = np.random.permutation(len(demand) - 1) + 1
        population.append(chromosome)
    return population

# 计算染色体的适应度，即路径长度
def fitness(chromosome, distance_matrix, demand, capacity):
    path_demand = np.cumsum(demand[chromosome])
    path_demand = np.insert(path_demand, 0, 0)
    path_demand = np.delete(path_demand, -1)
    path_capacity = np.maximum(path_demand, np.roll(path_demand, shift=1))
    path_capacity[0] = 0
    path_distance = distance_matrix[0, chromosome[0]] + np.sum(distance_matrix[chromosome[:-1], chromosome[1:]]) + distance_matrix[chromosome[-1], 0]
    path_penalty = np.sum(np.maximum(path_demand - path_capacity, 0))
    if path_penalty > 0 or path_distance > capacity:
        return 0
    else:
        return 1 / path_distance

# 选择精英个体
def select_elite(population, elite_size, distance_matrix, demand, capacity):
    fitness_scores = [(fitness(chromosome, distance_matrix, demand, capacity), chromosome) for chromosome in population]
    fitness_scores.sort(reverse=True)
    elite = []
    for i in range(elite_size):
        elite.append(fitness_scores[i][1])
    return elite

# 交叉操作
def crossover(parent1, parent2):
    crossover_point = random.randint(1, len(parent1) - 1)
    child1 = np.concatenate((parent1[:crossover_point], [x for x in parent2 if x not in parent1[:crossover_point]]))
    child2 = np.concatenate((parent2[:crossover_point], [x for x in parent1 if x not in parent2[:crossover_point]]))
    return (child1, child2)

# 变异操作
def mutate(chromosome, mutation_rate):
    for i in range(len(chromosome)):
        if random.random() < mutation_rate:
            j = random.randint(0, len(chromosome) - 1)
            chromosome[i], chromosome[j] = chromosome[j], chromosome[i]
    return chromosome

# 遗传算法主函数
def genetic_algorithm(distance_matrix, demand, capacity, population_size, elite_size, mutation_rate, generations):
    population = initial_population(population_size, demand)
    for generation in range(generations):
        elite = select_elite(population, elite_size, distance_matrix, demand, capacity)
        new_population = elite
        while len(new_population) < population_size:
            parent1, parent2 = random.sample(elite, 2)
            child1, child2 = crossover(parent1, parent2)
            child1 = mutate(child1, mutation_rate)
            child2 = mutate(child2, mutation_rate)
            new_population.append(child1)
            new_population.append(child2)
        population = new_population
    best_chromosome = elite[0]
    best_fitness = fitness(best_chromosome, distance_matrix, demand, capacity)
    return (best_chromosome, best_fitness)

# 运行遗传算法
best_chromosome, best_fitness = genetic_algorithm(distance_matrix, demand, capacity, population_size, elite_size, mutation_rate, generations)
print('Best Chromosome:', best_chromosome)
print('Best Fitness:', best_fitness)

上述代码中的距离矩阵、需求量和车辆容量限制可以根据具体问题进行修改。遗传算法的参数可以根据实际情况进行调整。