python实现遗传算法tsp旅行商问题

以下是Python实现遗传算法解决TSP旅行商问题的示例代码：

import random

# 城市坐标
city_pos = [(60, 200), (180, 200), (80, 180), (140, 180), (20, 160), (100, 160), (200, 160), (140, 140), (40, 120), (100, 120), (180, 100), (60, 80), (120, 80), (180, 60), (20, 40), (100, 40), (200, 40), (20, 20), (60, 20), (160, 20)]

# 种群大小
POP_SIZE = 500

# 城市数量
ITY_COUNT = len(city_pos)

# 交叉概率
CROSS_RATE = 0.1

# 变异概率
MUTATION_RATE = 0.02

# 代数
N_GENERATIONS = 500

# 计算两个城市之间的距离
def distance(city1, city2):
    return ((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2) ** 0.5

# 计算一条路径的总距离
def get_fitness(path):
    distance_sum = 0
    for i in range(CITY_COUNT - 1):
        distance_sum += distance(city_pos[path[i]], city_pos[path[i+1]])
    distance_sum += distance(city_pos[path[-1]], city_pos[path[0]])
    return 1 / distance_sum

# 初始化种群
def init_population():
    population = []
    for i in range(POP_SIZE):
        path = list(range(CITY_COUNT))
        random.shuffle(path)
        population.append(path)
    return population

# 选择
def select(population, fitness):
    idx = random.randint(0, POP_SIZE - 1)
    for i in range(POP_SIZE):
        if random.random() < fitness[i] / fitness.sum():
            idx = i
            break
    return population[idx]

# 交叉
def crossover(parent1, parent2):
    if random.random() < CROSS_RATE:
        child = [-1] * CITY_COUNT
        start = random.randint(0, CITY_COUNT - 1)
        end = random.randint(start, CITY_COUNT - 1)
        child[start:end+1] = parent1[start:end+1]
        for i in range(CITY_COUNT):
            if parent2[i] not in child:
                for j in range(CITY_COUNT):
                    if child[j] == -1:
                        child[j] = parent2[i]
                        break
        return child
    else:
        return parent1

# 变异
def mutate(child):
    if random.random() < MUTATION_RATE:
        idx1, idx2 = random.sample(range(CITY_COUNT), 2)
        child[idx1], child[idx2] = child[idx2], child[idx1]
    return child

# 遗传算法主函数
def genetic_algorithm():
    population = init_population()
    for generation in range(N_GENERATIONS):
        fitness = [get_fitness(path) for path in population]
        best_path = population[fitness.index(max(fitness))]
        print("Generation:", generation, "| Best path length:", 1 / max(fitness))
        new_population = [best_path]
        for i in range(POP_SIZE - 1):
            parent1 = select(population, fitness)
            parent2 = select(population, fitness)
            child = crossover(parent1, parent2)
            child = mutate(child)
            new_population.append(child)
        population = new_population
    return best_path

# 运行遗传算法
best_path = genetic_algorithm()
print("Best path:", best_path)