请以import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import copy import math import random import time from multiprocessing import Pool as ThreadPool #读取数据 path1='att48.tsp' path2='eil76.tsp' path3='pcb442.tsp' path4='rd100.tsp' path5='tsp225.tsp' def readcity(path): df = pd.read_csv("C:\文件\现代优化算法\TSP训练数据集\"+path, sep=" ", skiprows=6, header=None) return df #选择文件 df = readcity(path4) df.head() city = np.array(range(1,len(df[0][0:len(df)-1])+1)) city_x = np.array(df[1][0:len(df)-1]) city_y = np.array(df[2][0:len(df)-1])为开头补充完成一个模拟退火求解tsp问题的代码，我发给你的代码是用于获取输入数据并将其转化成坐标的功能。要求求解过程中用2-opt邻域构造法，在内循环中用Metropolis准则判断是否接受解来实现等温过程。

好的，以下是使用模拟退火算法求解TSP问题的代码：

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import copy
import math
import random
import time
from multiprocessing import Pool as ThreadPool

# 读取数据
path1 = 'att48.tsp'
path2 = 'eil76.tsp'
path3 = 'pcb442.tsp'
path4 = 'rd100.tsp'
path5 = 'tsp225.tsp'

def readcity(path):
    df = pd.read_csv("C:\文件\现代优化算法\TSP训练数据集\"+path, sep=" ", skiprows=6, header=None)
    return df

# 选择文件
df = readcity(path4)
city = np.array(range(1,len(df[0][0:len(df)-1])+1))
city_x = np.array(df[1][0:len(df)-1])
city_y = np.array(df[2][0:len(df)-1])

# 生成初始解
init_solution = np.concatenate(([1], np.random.permutation(city[1:]), [1]))

# 计算初始解的总路程
def calculate_distance(solution):
    distance = 0
    for i in range(len(solution)-1):
        distance += math.sqrt((city_x[solution[i]-1] - city_x[solution[i+1]-1])**2 + (city_y[solution[i]-1] - city_y[solution[i+1]-1])**2)
    return distance

init_distance = calculate_distance(init_solution)

# 定义2-opt邻域操作
def swap(solution, i, j):
    new_solution = copy.deepcopy(solution)
    new_solution[i:j+1] = np.flip(solution[i:j+1])
    return new_solution

# 定义Metropolis准则
def metropolis(delta, temperature):
    if delta < 0:
        return True
    else:
        p = math.exp(-delta/temperature)
        return np.random.choice([True, False], p=[p, 1-p])

# 定义模拟退火函数
def simulated_annealing(init_solution, init_distance, max_iterations, max_temperature, min_temperature, cooling_rate):
    current_solution = init_solution
    current_distance = init_distance
    best_solution = init_solution
    best_distance = init_distance
    temperature = max_temperature
    iteration = 0
    
    while temperature > min_temperature and iteration < max_iterations:
        i, j = sorted(np.random.choice(range(1, len(city)), size=2, replace=False))
        new_solution = swap(current_solution, i, j)
        new_distance = calculate_distance(new_solution)
        
        if metropolis(new_distance-current_distance, temperature):
            current_solution, current_distance = new_solution, new_distance
            
        if current_distance < best_distance:
            best_solution, best_distance = current_solution, current_distance
            
        temperature *= cooling_rate
        iteration += 1
        
    return best_solution, best_distance

# 运行模拟退火算法
max_iterations = 10000
max_temperature = 10000
min_temperature = 1
cooling_rate = 0.99

best_solution, best_distance = simulated_annealing(init_solution, init_distance, max_iterations, max_temperature, min_temperature, cooling_rate)

# 打印最优解和最优解的总路程
print("Best solution:", best_solution)
print("Best distance:", best_distance)

# 可视化最优解
best_city_x = city_x[best_solution-1]
best_city_y = city_y[best_solution-1]
plt.plot(best_city_x, best_city_y, 'o-')
plt.title("Simulated Annealing TSP Solution")
plt.show()

注意：由于你的路径可能与我的不同，需要根据实际情况修改读取数据的路径。