TSP/TS.py

import random
import math
import numpy as np
import matplotlib.pyplot as plt
import time

class TS(object):
    def __init__(self, num_city, data,taboo_size=5, iteration=500):
        self.taboo_size = taboo_size
        self.iteration = iteration
        self.num_city = num_city
        self.location = data
        self.taboo = []

        self.dis_mat = self.compute_dis_mat(num_city, data)
        self.path = self.greedy_init(self.dis_mat,100,num_city)
        self.best_path = self.path
        self.cur_path = self.path
        self.best_length = self.compute_pathlen(self.path, self.dis_mat)

        # 显示初始化后的路径
        init_pathlen = 1. / self.compute_pathlen(self.path, self.dis_mat)
        # 存储结果，画出收敛图
        self.iter_x = [0]
        self.iter_y = [1. / init_pathlen]
    def greedy_init(self, dis_mat, num_total, num_city):
        start_index = 0
        result = []
        for i in range(num_total):
            rest = [x for x in range(0, num_city)]
            if start_index >= num_city:
                start_index = np.random.randint(0, num_city)
                result.append(result[start_index].copy())
                continue
            current = start_index
            rest.remove(current)
            result_one = [current]   # 找到一条最近邻路径
            while len(rest) != 0:
                tmp_min = math.inf
                tmp_choose = -1
                for x in rest:
                    if dis_mat[current][x] < tmp_min:
                        tmp_min = dis_mat[current][x]
                        tmp_choose = x
                current = tmp_choose
                result_one.append(tmp_choose)
                rest.remove(tmp_choose)
            result.append(result_one)
            start_index += 1
        pathlens = self.compute_paths(result)
        sortindex = np.argsort(pathlens)
        index = sortindex[0]
        return result[index]
        # return result[0]

    # 初始化一条随机路径
    def random_init(self, num_city):
        tmp = [x for x in range(num_city)]
        random.shuffle(tmp)
        return tmp

    # 计算不同城市之间的距离
    def compute_dis_mat(self, num_city, location):
        dis_mat = np.zeros((num_city, num_city))
        for i in range(num_city):
            for j in range(num_city):
                if i == j:
                    dis_mat[i][j] = np.inf
                    continue
                a = location[i]
                b = location[j]
                tmp = np.sqrt(sum([(x[0] - x[1]) ** 2 for x in zip(a, b)]))
                dis_mat[i][j] = tmp
        return dis_mat

    # 计算路径长度
    def compute_pathlen(self, path, dis_mat):
        a = path[0]
        b = path[-1]
        result = dis_mat[a][b]
        for i in range(len(path) - 1):
            a = path[i]
            b = path[i + 1]
            result += dis_mat[a][b]
        return result

    # 计算一个群体的长度
    def compute_paths(self, paths):
        result = []
        for one in paths:
            length = self.compute_pathlen(one, self.dis_mat)
            result.append(length)
        return result

    # 产生随机解
    def ts_search(self, x):
        moves = []
        new_paths = []
        while len(new_paths)<400:
            i = np.random.randint(len(x))
            j = np.random.randint(len(x))
            tmp = x.copy()
            tmp[i:j] = tmp[i:j][::-1]
            new_paths.append(tmp)
            moves.append([i, j])
        return new_paths, moves

    # 禁忌搜索
    def ts(self):
        start_time = time.time()
        for cnt in range(self.iteration):
            new_paths, moves = self.ts_search(self.cur_path)
            new_lengths = self.compute_paths(new_paths)
            sort_index = np.argsort(new_lengths)
            min_l = new_lengths[sort_index[0]]
            min_path = new_paths[sort_index[0]]
            min_move = moves[sort_index[0]]

            # 更新当前的最优路径
            if min_l < self.best_length:
                self.best_length = min_l
                self.best_path = min_path
                self.cur_path = min_path
                # 更新禁忌表
                if min_move in self.taboo:
                    self.taboo.remove(min_move)

                self.taboo.append(min_move)
            else:
                # 找到不在禁忌表中的操作
                while min_move in self.taboo:
                    sort_index = sort_index[1:]
                    min_path = new_paths[sort_index[0]]
                    min_move = moves[sort_index[0]]
                self.cur_path = min_path
                self.taboo.append(min_move)
            # 禁忌表超长了
            if len(self.taboo) > self.taboo_size:
                self.taboo = self.taboo[1:]
            self.iter_x.append(cnt)
            self.iter_y.append(self.best_length)
            #print(cnt, self.best_length)
        print(self.best_length)
        total_time = time.time() - start_time
        return self.best_length, total_time

    def run(self):
        best_length, total_time = self.ts()
        return self.location[self.best_path], best_length, total_time

def read_tsp(path):
    lines = open(path, 'r').readlines()
    assert 'NODE_COORD_SECTION\n' in lines
    index = lines.index('NODE_COORD_SECTION\n')
    data = lines[index + 1:-1]
    tmp = []
    for line in data:
        line = line.strip().split(' ')
        if line[0] == 'EOF':
            continue
        tmpline = []
        for x in line:
            if x == '':
                continue
            else:
                tmpline.append(float(x))
        if tmpline == []:
            continue
        tmp.append(tmpline)
    data = tmp
    return data

def parameter_sensitivity_test():
    # 定义参数组合
    taboo_sizes = [3, 5, 10]
    iterations = [200, 500, 1000]
    results = []

    # 遍历所有参数组合
    for taboo_size in taboo_sizes:
        for iteration in iterations:
            model = TS(num_city=data.shape[0], data=data.copy(),
                      taboo_size=taboo_size, iteration=iteration)
            best_length, total_time = model.ts()  # 调用ts()并获取结果
            results.append({
                "taboo_size": taboo_size,
                "iteration": iteration,
                "best_length": best_length,
                "time": total_time
            })
            print(f"taboo_size={taboo_size}, iteration={iteration}, "
                  f"length={best_length:.2f}, time={total_time:.2f}s")

    # 结果可视化（示例：热力图）
    import pandas as pd
    import seaborn as sns
    df = pd.DataFrame(results)
    pivot_length = df.pivot(index="taboo_size", columns="iteration", values="best_length")
    pivot_time = df.pivot(index="taboo_size", columns="iteration", values="time")

    plt.figure(figsize=(12, 5))
    plt.subplot(1, 2, 1)
    sns.heatmap(pivot_length, annot=True, fmt=".2f", cmap="YlGnBu")
    plt.title("Path Length vs Parameters")
    plt.subplot(1, 2, 2)
    sns.heatmap(pivot_time, annot=True, fmt=".2f", cmap="YlGnBu")
    plt.title("Time Cost vs Parameters")
    plt.tight_layout()
    plt.show()

if __name__ == "__main__":
    data = read_tsp('data/st70.tsp')

    data = np.array(data)
    plt.suptitle('TS in st70.tsp')
    data = data[:, 1:]
    plt.subplot(2, 2, 1)
    plt.title('raw data')
    show_data = np.vstack([data, data[0]])
    plt.plot(data[:, 0], data[:, 1])

    model = TS(num_city=data.shape[0], data=data.copy())
    Best_path, Best_length,execution_time = model.run()
    print("时间：",execution_time)

    Best_path = np.vstack([Best_path, Best_path[0]])
    fig, axs = plt.subplots(2, 1, sharex=False, sharey=False)
    axs[0].scatter(Best_path[:, 0], Best_path[:,1])
    Best_path = np.vstack([Best_path, Best_path[0]])
    axs[0].plot(Best_path[:, 0], Best_path[:, 1])
    axs[0].set_title('规划结果')
    iterations = model.iter_x
    best_record = model.iter_y
    axs[1].plot(iterations, best_record)
    axs[1].set_title('收敛曲线')
    #plt.show()
    plt.show(block=False)  # 添加此行，允许后续代码继续执行
    #参数敏感性实验
    parameter_sensitivity_test()