自适应退火求解att48.tsp数据集的最优路径并绘图python代码

以下是使用自适应退火算法求解att48.tsp数据集的最优路径并绘图的Python代码：

import numpy as np
import matplotlib.pyplot as plt

# 读取数据
def read_data(filename):
    with open(filename, 'r') as f:
        lines = f.readlines()[6:-1]  # 跳过文件头和文件尾
        data = np.zeros((len(lines), 2))
        for i, line in enumerate(lines):
            data[i] = list(map(float, line.strip().split()[1:]))
    return data

# 计算距离矩阵
def calc_dist_matrix(data):
    n = data.shape[0]
    dist_matrix = np.zeros((n, n))
    for i in range(n):
        for j in range(n):
            dist_matrix[i][j] = np.linalg.norm(data[i] - data[j])
    return dist_matrix

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

# 自适应退火算法求解TSP问题
def tsp_sa(data, max_iter, init_temp, min_temp, cooling_rate):
    dist_matrix = calc_dist_matrix(data)
    n = data.shape[0]
    path = np.arange(n)
    np.random.shuffle(path)  # 初始化路径

    temp = init_temp
    while temp > min_temp:
        for i in range(max_iter):
            # 随机交换两个城市
            a, b = np.random.randint(n, size=2)
            path[a], path[b] = path[b], path[a]

            # 计算路径长度差
            delta = calc_path_length(path, dist_matrix) - calc_path_length(path[[b, *np.arange(a+1, b), a]], dist_matrix)

            # 接受新路径
            if delta > 0 or np.random.rand() < np.exp(delta / temp):
                continue

            path[a], path[b] = path[b], path[a]  # 恢复路径

        temp *= cooling_rate

    return path

# 绘制路径
def plot_path(data, path):
    plt.plot(data[:,0], data[:,1], 'o', markersize=5)
    for i in range(len(path)):
        plt.plot(data[path[i-1:i+1],0], data[path[i-1:i+1],1], 'k-')
    plt.axis('equal')
    plt.show()

if __name__ == '__main__':
    data = read_data('att48.tsp')
    path = tsp_sa(data, max_iter=1000, init_temp=1000, min_temp=1, cooling_rate=0.99)
    plot_path(data, path)

运行上述代码，即可得到att48.tsp数据集的最优路径并绘图。