用python模拟退火算法去解决TSP问题的代码

以下是使用Python实现模拟退火算法解决TSP问题的示例代码：

import random
import math


def distance(point1, point2):
    """计算两点之间的距离"""
    return math.sqrt((point1[0] - point2[0]) ** 2 + (point1[1] - point2[1]) ** 2)


def total_distance(points, order):
    """计算路径总长度"""
    dist = 0
    for i in range(-1, len(points) - 1):
        dist += distance(points[order[i]], points[order[i+1]])
    return dist


def anneal(points, temp, temp_min, alpha):
    """模拟退火算法"""
    order = list(range(len(points)))
    random.shuffle(order)
    current_dist = total_distance(points, order)
    while temp > temp_min:
        for i in range(100):
            # 生成新解
            new_order = order.copy()
            index1, index2 = random.sample(range(len(points)), 2)
            new_order[index1], new_order[index2] = new_order[index2], new_order[index1]
            new_dist = total_distance(points, new_order)
            # 计算能量差
            delta = new_dist - current_dist
            # 判断是否接受新解
            if delta < 0 or math.exp(-delta / temp) > random.random():
                order = new_order
                current_dist = new_dist
        temp *= alpha
    return order


if __name__ == '__main__':
    # 随机生成10个点
    points = [(random.uniform(0, 1), random.uniform(0, 1)) for _ in range(10)]
    # 设置初始温度、最低温度和衰减系数
    temp = 100
    temp_min = 0.01
    alpha = 0.99
    # 调用模拟退火算法求解TSP问题
    order = anneal(points, temp, temp_min, alpha)
    print(order)
    print(total_distance(points, order))

代码中将距离计算、路径总长度计算、模拟退火算法三个部分分别封装成了函数。在`anneal`函数中，首先生成一个随机初始解，然后不断降温，在每个温度下进行100次新解的生成和接受过程，直到温度降到最低温度为止。最终返回最优解。在主函数中，我们随机生成了10个点，并调用`anneal`函数求解TSP问题。