python代码用遗传算法求解车辆路径规划问题

车辆路径规划问题是一种NP难问题，遗传算法是一种常用的全局优化算法，可以用来解决这类问题。下面是一个基于Python的遗传算法求解车辆路径规划问题的代码示例：

import random

# 路径规划问题的目标函数
def fitness(route, distance_matrix):
    total_distance = 0
    for i in range(len(route) - 1):
        total_distance += distance_matrix[route[i]][route[i+1]]
    return total_distance

# 遗传算法求解路径规划问题
def genetic_algorithm(distance_matrix, population_size=100, max_generation=1000, mutation_rate=0.01):
    # 初始化种群
    population = []
    for i in range(population_size):
        route = list(range(1, len(distance_matrix)))
        random.shuffle(route)
        population.append(route)

    # 迭代求解
    for generation in range(max_generation):
        # 计算适应度
        fitness_values = [fitness(route, distance_matrix) for route in population]
        best_fitness = min(fitness_values)
        best_route = population[fitness_values.index(best_fitness)]

        # 输出每一代的最优解
        print("Generation %d: shortest distance = %d" % (generation, best_fitness))

        # 选择
        parents = []
        for i in range(population_size):
            index1 = random.randint(0, population_size-1)
            index2 = random.randint(0, population_size-1)
            if fitness_values[index1] < fitness_values[index2]:
                parents.append(population[index1])
            else:
                parents.append(population[index2])

        # 交叉
        children = []
        for i in range(population_size):
            parent1 = parents[random.randint(0, population_size-1)]
            parent2 = parents[random.randint(0, population_size-1)]
            child = [0] * len(distance_matrix)
            start = random.randint(0, len(distance_matrix)-1)
            end = random.randint(0, len(distance_matrix)-1)
            if start > end:
                start, end = end, start
            child[start:end+1] = parent1[start:end+1]
            for j in range(len(parent2)):
                if parent2[j] not in child:
                    for k in range(len(child)):
                        if child[k] == 0:
                            child[k] = parent2[j]
                            break
            children.append(child)

        # 变异
        for i in range(population_size):
            if random.random() < mutation_rate:
                index1 = random.randint(0, len(distance_matrix)-1)
                index2 = random.randint(0, len(distance_matrix)-1)
                children[i][index1], children[i][index2] = children[i][index2], children[i][index1]

        population = children

    # 返回最优解
    fitness_values = [fitness(route, distance_matrix) for route in population]
    best_fitness = min(fitness_values)
    best_route = population[fitness_values.index(best_fitness)]
    return best_route, best_fitness

在这个代码中，`distance_matrix`是一个二维数组，表示每个城市之间的距离。`population_size`是种群大小，`max_generation`是最大迭代次数，`mutation_rate`是变异率。函数`fitness`计算一个路径的总距离。函数`genetic_algorithm`实现了遗传算法的主要过程：初始化种群、计算适应度、选择、交叉、变异。整个算法迭代`max_generation`次，每一代输出当前最优解的距离。最后返回最优解的路径和距离。