遗传算法求解tsp问题代码

遗传算法是一种基于自然选择和遗传机制的优化搜索技术，常用于解决复杂问题，如旅行商问题（Traveling Salesman Problem, TSP）。TSP是一个经典的组合优化问题，目标是找到访问一组城市并返回起点的最短路径。

以下是使用Python的一个简单遗传算法求解TSP问题的代码概述：

import numpy as np

# 定义遗传算法相关参数
pop_size = 100 # 群体大小
num_generations = 1000 # 迭代次数
elite_ratio = 0.1 # 保留精英比例
mutation_rate = 0.01 # 变异概率

def initialize_population(city_list): 
    return [np.random.permutation(city_list) for _ in range(pop_size)]

def fitness(individual, city_list):
    # 计算路径长度作为适应度函数
    return sum(cities.distance(city_list[i], city_list[j]) for i, j in zip(individual, individual[1:] + [individual]))

def selection(population, fitness_values):
    elite_indices = np.argsort(fitness_values)[:int(elite_ratio * pop_size)]
    parents = np.concatenate([population[elite_indices], population[np.random.choice(range(len(population)), size=(pop_size - len(elite_indices),))]])
    return parents

def crossover(parents):
    offspring = []
    for _ in range(int(pop_size / 2)):
        parent1, parent2 = np.random.choice(parents, size=2)
        cut_point = np.random.randint(0, len(parent1) - 1)
        child = np.concatenate((parent1[:cut_point], parent2[cut_point:]))
        offspring.append(child)
    return offspring

def mutation(offspring, mutation_rate):
    mutated_offspring = []
    for child in offspring:
        if np.random.rand() < mutation_rate:
            swap_points = np.random.choice(range(len(child)), size=2, replace=False)
            child[[swap_points[0], swap_points]] = child[[swap_points, swap_points]]
        mutated_offspring.append(child)
    return mutated_offspring

def genetic_algorithm(city_list):
    population = initialize_population(city_list)
    
    for _ in range(num_generations):
        fitness_values = [fitness(individual, city_list) for individual in population]
        parents = selection(population, fitness_values)
        offspring = crossover(parents)
        offspring = mutation(offspring, mutation_rate)
        
        # 更新种群
        population = offspring + [parent for parent in parents if parent not in offspring]

    best_individual = min(population, key=lambda x: fitness(x, city_list))
    return best_individual, fitness(best_individual, city_list)

# 示例城市列表
cities = ...  # 城市坐标数据结构

best_route, shortest_distance = genetic_algorithm(cities)
print("Best route:", best_route)
print("Shortest distance:", shortest_distance)

这个代码只是一个基本框架，实际应用中可能需要根据具体的城市数据结构（如经纬度表示）进行调整，并可能加入更多细节，比如解编码、初始化策略等。运行这个程序后，它将返回一条近似最优的旅行路线以及对应的总距离。