写一个用遗传算法解决VRP问题的代码

下面是一个使用 Python 实现的遗传算法解决 VRP 问题的代码示例：

import random
import numpy as np

class VRP:
    def __init__(self, num_customers, vehicle_capacity, demand, distance_matrix):
        self.num_customers = num_customers
        self.vehicle_capacity = vehicle_capacity
        self.demand = demand
        self.distance_matrix = distance_matrix
    
    def fitness(self, individual):
        # 计算路径长度
        path_length = 0
        current_capacity = self.vehicle_capacity
        for i in range(self.num_customers + 1):
            from_node = individual[i]
            to_node = individual[i + 1]
            path_length += self.distance_matrix[from_node][to_node]
            current_capacity -= self.demand[to_node]
            if current_capacity < 0:
                return float('inf')
        return path_length
    
    def generate_random_individual(self):
        # 生成随机的路径
        individual = np.arange(self.num_customers + 1)
        np.random.shuffle(individual[1:])
        return individual
    
    def mutate(self, individual):
        # 变异
        node1, node2 = np.random.choice(individual, 2, replace=False)
        individual[node1], individual[node2] = individual[node2], individual[node1]
        return individual
    
    def crossover(self, individual1, individual2):
        # 交叉
        cut_point = np.random.randint(1, self.num_customers)
        new_individual = np.concatenate((individual1[:cut_point], individual2[cut_point:]))
        new_individual = np.unique(new_individual)
        if len(new_individual) < self.num_customers + 1:
            remaining = np.setdiff1d(np.arange(self.num_customers + 1), new_individual)
            new_individual = np.concatenate((new_individual, remaining))
        return new_individual
    
def genetic_algorithm(vrp, population_size=100, mutation_prob=0.1, num_generations=1000):
    population = [vrp.generate_random_individual() for _ in range(population_size)]
    for generation in range(num_generations):
        # 计算适应度
        fitness = [vrp.fitness(individual) for individual in population]
        best_individual = population[np.argmin(fitness)]
        if min(fitness) == 0:
            break
        
        # 选择
        population = [population[i] for i in np.argsort(fitness)[:population_size]]
        
        # 交叉