帮我用python写一个用模拟退火算法解决tsp问题的代码

好的，以下是用 Python 实现模拟退火算法解决 TSP 问题的代码：

import math
import random

# 旅行商城市数量
NUM_CITIES = 20

# 遗传算法迭代次数
NUM_ITERATIONS = 10000

# 退火降温比率
COOLING_RATE = 0.999

# 起始温度
START_TEMPERATURE = 10000

# 初始解
initial_solution = list(range(NUM_CITIES))


# 计算距离
def distance(city1, city2):
    x1, y1 = city1
    x2, y2 = city2
    return math.sqrt((x1-x2)**2 + (y1-y2)**2)


# 初始评分函数
def initial_score(city_list):
    total_distance = 0
    for i in range(NUM_CITIES):
        city1 = city_list[i % NUM_CITIES]
        city2 = city_list[(i+1) % NUM_CITIES]
        total_distance += distance(city1, city2)
    return total_distance


# 产生邻居解
def generate_neighbor(city_list):
    neighbor = city_list.copy()
    i = random.randint(0, NUM_CITIES-1)
    j = random.randint(0, NUM_CITIES-1)
    neighbor[i], neighbor[j] = neighbor[j], neighbor[i]
    return neighbor


# 评估函数
def evaluate(city_list, temperature):
    current_score = initial_score(city_list)
    neighbor = generate_neighbor(city_list)
    neighbor_score = initial_score(neighbor)
    delta = neighbor_score - current_score
    if delta < 0:
        return neighbor, neighbor_score
    else:
        probability = math.exp(-delta/temperature)
        if random.random() < probability:
            return neighbor, neighbor_score
        else:
            return city_list, current_score


# 模拟退火函数
def simulated_annealing():
    current_solution = initial_solution
    current_score = initial_score(current_solution)
    temperature = START_TEMPERATURE
    for i in range(NUM_ITERATIONS):
        temperature *= COOLING_RATE
        current_solution, current_score = evaluate(current_solution, temperature)
    return current_solution, current_score


# 生成城市坐标
cities = [(random.random(), random.random()) for i in range(NUM_CITIES)]

# 模拟退火求解
solution, score = simulated_annealing()

print(cities)
print(solution)
print(score)

此代码实现了模拟退火算法用于解决 TSP 问题。该算法通过产生邻居解，并根据当前温度、评估函数和概率来采纳或拒绝邻居解，从而在退火过程中搜索全局最优解。