使用启发式算法解决TSP问题代码

以下是使用遗传算法解决TSP问题的示例代码：

import random
import numpy as np

# 数据集
cities = np.array([[0, 1, 2, 3, 4, 5, 6],
                   [1, 0, 4, 2, 8, 3, 6],
                   [2, 4, 0, 5, 1, 7, 3],
                   [3, 2, 5, 0, 6, 4, 1],
                   [4, 8, 1, 6, 0, 5, 2],
                   [5, 3, 7, 4, 5, 0, 6],
                   [6, 6, 3, 1, 2, 6, 0]])

# 遗传算法参数
POPULATION_SIZE = 20  # 种群大小
MUTATION_RATE = 0.1  # 变异率
GENERATIONS = 100  # 迭代次数


# 计算路径长度
def get_path_length(path):
    length = 0
    for i in range(len(path) - 1):
        length += cities[path[i], path[i+1]]
    length += cities[path[-1], path[0]]
    return length


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


# 选择
def selection(population):
    fitness = [1 / get_path_length(p) for p in population]
    idx = random.choices(range(len(population)), weights=fitness, k=2)
    return population[idx[0]], population[idx[1]]


# 交叉
def crossover(p1, p2):
    n = len(p1)
    idx1, idx2 = sorted(random.sample(range(n), 2))
    temp = [-1] * n
    for i in range(idx1, idx2+1):
        temp[i] = p1[i]
    for i in range(n):
        if p2[i] not in temp:
            for j in range(n):
                if temp[j] == -1:
                    temp[j] = p2[i]
                    break
    return temp


# 变异
def mutation(path):
    n = len(path)
    idx1, idx2 = sorted(random.sample(range(n), 2))
    path[idx1], path[idx2] = path[idx2], path[idx1]
    return path


# 遗传算法主程序
def genetic_algorithm():
    # 初始化种群
    population = init_population(POPULATION_SIZE, len(cities))

    # 迭代
    for i in range(GENERATIONS):
        # 选择、交叉、变异
        new_population = []
        for j in range(POPULATION_SIZE):
            p1, p2 = selection(population)
            child = crossover(p1, p2)
            if random.random() < MUTATION_RATE:
                child = mutation(child)
            new_population.append(child)

        # 更新种群
        population = new_population

        # 输出结果
        best_path = min(population, key=get_path_length)
        print('Generation {}: best path length = {}'.format(i+1, get_path_length(best_path)))


if __name__ == '__main__':
    genetic_algorithm()

该代码实现了遗传算法解决TSP问题的基本框架，其中包含初始化种群、选择、交叉、变异等操作。通过调节参数，可以得到更好的解决方案。