并行退火求解att48.tsp数据集的最优路径并绘图python代码

以下是使用并行退火算法求解att48.tsp数据集的最优路径并绘制图形的Python代码：

import numpy as np
import matplotlib.pyplot as plt
from multiprocessing import Pool

# 读取数据集
def read_data(file_name):
    with open(file_name, 'r') as f:
        lines = f.readlines()[6:-1]
        data = []
        for line in lines:
            x, y = map(float, line.strip().split()[1:])
            data.append([x, y])
        return np.array(data)

# 计算路径长度
def get_distance(route, dist_mat):
    dist = 0
    for i in range(len(route) - 1):
        dist += dist_mat[route[i], route[i+1]]
    dist += dist_mat[route[-1], route[0]]
    return dist

# 生成初始解
def generate_init_solution(num_cities):
    return np.random.permutation(num_cities)

# 退火过程
def anneal(route, dist_mat, temp, cooling_rate):
    dist_cur = get_distance(route, dist_mat)
    while temp > 1:
        new_route = np.copy(route)
        i, j = np.random.randint(0, len(route), size=2)
        new_route[i], new_route[j] = new_route[j], new_route[i]
        dist_new = get_distance(new_route, dist_mat)
        if dist_new < dist_cur:
            route = np.copy(new_route)
            dist_cur = dist_new
        else:
            prob = np.exp(-(dist_new - dist_cur) / temp)
            if np.random.rand() < prob:
                route = np.copy(new_route)
                dist_cur = dist_new
        temp *= cooling_rate
    return route, dist_cur

# 并行退火过程
def parallel_anneal(num_cities, dist_mat, temp, cooling_rate, num_processes):
    with Pool(num_processes) as pool:
        routes = [generate_init_solution(num_cities) for _ in range(num_processes)]
        results = [pool.apply_async(anneal, args=(route, dist_mat, temp, cooling_rate)) for route in routes]
        best_route = None
        best_dist = float('inf')
        for res in results:
            route, dist = res.get()
            if dist < best_dist:
                best_route = route
                best_dist = dist
    return best_route, best_dist

# 绘制路径图
def plot_route(route, cities):
    plt.plot(cities[route, 0], cities[route, 1], 'o-')
    plt.plot(cities[route[0], 0], cities[route[0], 1], 'ro')
    plt.title('Total distance: {:.2f}'.format(get_distance(route, dist_mat)))
    plt.show()

if __name__ == '__main__':
    # 读取数据集
    cities = read_data('att48.tsp')

    # 计算距离矩阵
    dist_mat = np.zeros((len(cities), len(cities)))
    for i in range(len(cities)):
        for j in range(i, len(cities)):
            dist = np.linalg.norm(cities[i] - cities[j])
            dist_mat[i, j] = dist
            dist_mat[j, i] = dist

    # 参数设置
    temp = 1e4
    cooling_rate = 0.999
    num_processes = 4

    # 并行退火
    best_route, best_dist = parallel_anneal(len(cities), dist_mat, temp, cooling_rate, num_processes)

    # 绘制路径图
    plot_route(best_route, cities)

该代码首先读取att48.tsp数据集，并计算距离矩阵。然后定义了生成初始解、计算路径长度、退火过程、并行退火过程和绘制路径图等函数。在并行退火过程中，使用了Python的多进程库Pool，将初始解分为多个子问题，并对每个子问题独立地运行模拟退火算法，最后将子问题的解合并为全局解。最后，调用plot_route函数绘制最优路径图。