使用遗传算法解决CVRP问题数据随机生成用deap库写出程序

以下是使用遗传算法解决CVRP问题的Python代码，使用deap库：

import random
from deap import base, creator, tools

# 定义问题
NUM_CUSTOMERS = 20  # 客户数量
CAPACITY = 10  # 车辆容量
MIN_COORD = 0  # 坐标最小值
MAX_COORD = 100  # 坐标最大值
CUSTOMER_DEMANDS = [random.randint(1, 5) for _ in range(NUM_CUSTOMERS)]  # 客户需求量
DEPOT_COORDS = (50, 50)  # 仓库坐标

# 定义遗传算法参数
POPULATION_SIZE = 50  # 种群大小
P_CROSSOVER = 0.9  # 交叉概率
P_MUTATION = 0.1  # 变异概率
MAX_GENERATIONS = 100  # 最大迭代次数

# 定义遗传算法框架
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)

toolbox = base.Toolbox()
toolbox.register("attr_customer", random.randint, 0, NUM_CUSTOMERS - 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_customer, n=NUM_CUSTOMERS)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mate", tools.cxOrdered)
toolbox.register("mutate", tools.mutShuffleIndexes, indpb=1.0/NUM_CUSTOMERS)
toolbox.register("select", tools.selTournament, tournsize=3)

def distance(coord1, coord2):
    """计算两点之间的距离"""
    return ((coord1[0] - coord2[0])**2 + (coord1[1] - coord2[1])**2)**0.5

def evaluate(individual):
    """评估个体适应度"""
    routes = []
    route = [0]  # 当前路线，初始为从仓库出发
    current_capacity = 0  # 当前车辆容量
    total_distance = 0  # 总行驶距离

    # 将客户按照个体中的顺序加入路线中
    for customer_index in individual:
        customer_demand = CUSTOMER_DEMANDS[customer_index]
        if current_capacity + customer_demand > CAPACITY:
            # 如果当前车辆容量无法满足当前客户需求，则新开一辆车
            route.append(0)  # 先回到仓库
            routes.append(route)
            route = [0, customer_index]
            current_capacity = customer_demand
        else:
            route.append(customer_index)
            current_capacity += customer_demand

    # 将最后一辆车的路线加入路线列表
    route.append(0)
    routes.append(route)

    # 计算总行驶距离
    for route in routes:
        for i in range(1, len(route)):
            distance_between_customers = distance(
                (coords[route[i-1]][0], coords[route[i-1]][1]),
                (coords[route[i]][0], coords[route[i]][1])
            )
            total_distance += distance_between_customers

    return (total_distance,)

# 注册适应度评估函数
toolbox.register("evaluate", evaluate)

# 生成坐标
coords = [random.sample(range(MIN_COORD, MAX_COORD), 2) for _ in range(NUM_CUSTOMERS)]

# 生成初始种群
population = toolbox.population(n=POPULATION_SIZE)

# 开始进化
for generation in range(MAX_GENERATIONS):
    # 评估种群适应度
    fitnesses = list(map(toolbox.evaluate, population))
    for ind, fit in zip(population, fitnesses):
        ind.fitness.values = fit

    # 打印最优解
    best_individual = tools.selBest(population, k=1)[0]
    print("Generation {}: Best distance = {}".format(generation, best_individual.fitness.values[0]))

    # 选择下一代
    offspring = toolbox.select(population, len(population))

    # 交叉
    for child1, child2 in zip(offspring[::2], offspring[1::2]):
        if random.random() < P_CROSSOVER:
            toolbox.mate(child1, child2)
            del child1.fitness.values
            del child2.fitness.values

    # 变异
    for mutant in offspring:
        if random.random() < P_MUTATION:
            toolbox.mutate(mutant)
            del mutant.fitness.values

    # 更新种群
    population = offspring

# 打印最终结果
best_individual = tools.selBest(population, k=1)[0]
print("Best distance = {}".format(best_individual.fitness.values[0]))
print("Best route = {}".format(best_individual))