tsp遗传算法变异算法代码

TSP（Traveling Salesman Problem，旅行商问题）是一个经典的组合优化问题，遗传算法是一种常用的解决TSP问题的方法之一。下面是一个简单的TSP遗传算法变异算法的代码示例：

import random

# 初始化种群
def init_population(num_cities, population_size):
    population = []
    for _ in range(population_size):
        individual = list(range(num_cities))
        random.shuffle(individual)
        population.append(individual)
    return population

# 计算路径长度
def calculate_distance(city1, city2):
    # 计算城市之间的距离，这里可以根据实际情况进行修改
    return distance

def calculate_fitness(individual):
    total_distance = 0
    for i in range(len(individual)):
        city1 = individual[i]
        city2 = individual[(i + 1) % len(individual)]
        total_distance += calculate_distance(city1, city2)
    return 1 / total_distance

# 选择操作
def selection(population, num_parents):
    parents = []
    fitness_values = [calculate_fitness(individual) for individual in population]
    for _ in range(num_parents):
        max_fitness_index = fitness_values.index(max(fitness_values))
        parents.append(population[max_fitness_index])
        fitness_values[max_fitness_index] = -1
    return parents

# 变异操作
def mutation(individual):
    # 随机选择两个位置进行交换
    index1 = random.randint(0, len(individual) - 1)
    index2 = random.randint(0, len(individual) - 1)
    individual[index1], individual[index2] = individual[index2], individual[index1]
    return individual

# 主函数
def tsp_ga(num_cities, population_size, num_generations):
    population = init_population(num_cities, population_size)
    for _ in range(num_generations):
        parents = selection(population, num_parents=2)
        offspring = [mutation(parent) for parent in parents]
        population = parents + offspring
    best_individual = max(population, key=calculate_fitness)
    best_distance = 1 / calculate_fitness(best_individual)
    return best_individual, best_distance

# 示例调用
num_cities = 10
population_size = 100
num_generations = 1000
best_individual, best_distance = tsp_ga(num_cities, population_size, num_generations)
print("Best individual:", best_individual)
print("Best distance:", best_distance)

这段代码实现了一个简单的TSP遗传算法变异算法。其中，`init_population`函数用于初始化种群，`calculate_distance`函数用于计算城市之间的距离，`calculate_fitness`函数用于计算个体的适应度，`selection`函数用于选择操作，`mutation`函数用于变异操作，`tsp_ga`函数是主函数，用于执行遗传算法的迭代过程。