多式联运路径规划的遗传算法代码

以下是一个基于遗传算法的多式联运路径规划的 Python 代码示例：

import numpy as np

# 定义常量
POP_SIZE = 50  # 种群数量
CHROM_SIZE = 20  # 染色体长度
CROSS_RATE = 0.8  # 交叉概率
MUTATION_RATE = 0.02  # 变异概率
N_GENERATIONS = 500  # 迭代次数
N_TRUCKS = 5  # 货车数量
N_CUSTOMERS = 10  # 客户数量
N_DEPOTS = 3  # 集散地数量

# 定义距离矩阵
distances = np.random.randint(1, 100, size=(N_CUSTOMERS+N_DEPOTS, N_CUSTOMERS+N_DEPOTS))
for i in range(N_CUSTOMERS+N_DEPOTS):
    for j in range(N_CUSTOMERS+N_DEPOTS):
        if i == j:
            distances[i,j] = 0
        elif i < N_DEPOTS and j < N_DEPOTS:
            distances[i,j] = 0
        elif i >= N_DEPOTS and j >= N_DEPOTS:
            distances[i,j] = 0
        else:
            distances[j,i] = distances[i,j]

# 定义种群类
class Population:
    def __init__(self, size):
        self.size = size
        self.chroms = np.zeros((size, CHROM_SIZE), dtype=int)
        for i in range(size):
            self.chroms[i] = np.random.permutation(range(CHROM_SIZE))

    def fitness(self):
        fitness = np.zeros(self.size)
        for i in range(self.size):
            chrom = self.chroms[i]
            fitness[i] = self.calc_fitness(chrom)
        return fitness

    def calc_fitness(self, chrom):
        # 将染色体分割成多个路径
        paths = np.split(chrom, np.where(chrom == 0)[0])
        paths = [path for path in paths if len(path) > 1]

        # 计算每个路径的距离
        distances_sum = 0
        for path in paths:
            start = path[0]
            end = path[-1]
            distances_sum += distances[start, end]
            for i in range(len(path)-1):
                distances_sum += distances[path[i], path[i+1]]

        # 计算每个货车的装载量
        loads = np.zeros(N_TRUCKS)
        for path in paths:
            for i in path[1:-1]:
                loads[i-1] += 1
        overload = sum([max(0, loads[i]-10) for i in range(N_TRUCKS)])

        # 计算适应度函数值
        fitness = 1 / (distances_sum + overload + 1)
        return fitness

    def select(self, fitness):
        idx = np.random.choice(range(self.size), size=self.size, replace=True, p=fitness/fitness.sum())
        return self.chroms[idx]

    def crossover(self, parent, pop):
        if np.random.rand() < CROSS_RATE:
            # 随机选择另一个父母
            i = np.random.randint(0, len(pop))
            mate = pop[i]

            # 随机选择交叉点
            cx_point1 = np.random.randint(0, CHROM_SIZE)
            cx_point2 = np.random.randint(0, CHROM_SIZE-1)
            if cx_point2 >= cx_point1:
                cx_point2 += 1

            # 交叉
            mask = np.zeros(CHROM_SIZE, dtype=bool)
            mask[cx_point1:cx_point2] = True
            tmp1, tmp2 = parent[~mask], mate[~mask]
            offspring1, offspring2 = np.zeros(CHROM_SIZE, dtype=int), np.zeros(CHROM_SIZE, dtype=int)
            offspring1[mask], offspring2[mask] = parent[mask], mate[mask]
            offspring1[~mask], offspring2[~mask] = tmp2, tmp1
            return offspring1, offspring2
        else:
            return parent, None

    def mutate(self, offspring):
        for i in range(CHROM_SIZE):
            if np.random.rand() < MUTATION_RATE:
                j = np.random.randint(0, CHROM_SIZE)
                offspring[i], offspring[j] = offspring[j], offspring[i]
        return offspring

    def evolve(self):
        fitness = self.fitness()
        new_pop = np.zeros((self.size, CHROM_SIZE), dtype=int)
        for i, parent in enumerate(self.select(fitness)):
            offspring1, offspring2 = self.crossover(parent, self.chroms)
            offspring = self.mutate(offspring1)
            new_pop[i] = offspring
            if offspring2 is not None and i+1 < self.size:
                new_pop[i+1] = self.mutate(offspring2)
        self.chroms = new_pop

# 初始化种群
pop = Population(POP_SIZE)

# 开始迭代
for i in range(N_GENERATIONS):
    pop.evolve()

# 打印最优解
fitness = pop.fitness()
best_idx = np.argmax(fitness)
best_chrom = pop.chroms[best_idx]
paths = np.split(best_chrom, np.where(best_chrom == 0)[0])
paths = [path for path in paths if len(path) > 1]
print(f"Best Chromosome: {best_chrom}")
print(f"Best Fitness: {fitness[best_idx]}")
print(f"Paths: {paths}")

需要注意的是，上述代码仅为示例代码，并不保证能够得到最优解。实际应用中，需要根据具体情况进行参数调整和算法优化。