模拟退火算法解决TSP（python实现 110+行代码）【gif生成】

ゝ一世哀愁。 2022-04-10 01:55 294阅读 0赞

## 简述 ##

代码我是基于我之前写的两篇，一篇是遗传算法TSP的Python实现，一篇是模拟退火算法的解决TSP的C++实现。

*  [模拟退火算法理论+Python解决函数极值+C++实现解决TSP问题][Python_C_TSP]
 *  [遗传算法解决TSP问题 Python实现【160行以内代码】][TSP_ Python_160]

## 效果演示 ##

![在这里插入图片描述][20181219171938363.gif]

## 对比 ##

相比于遗传算法来说没有保持历史中的较优数据，但是通过退火的算法思维，很邻近点搜索的想法，任然能保持较为正确的收敛结果

## 代码 ##

import numpy as np
    import random
    import matplotlib.pyplot as plt
    import os
    import shutil
    import imageio
    
    
    def create_data(N, xu=25, yu=25, xd=-25, yd=-25):
        fx = lambda: random.random() * (xu - xd) + xd
        fy = lambda: random.random() * (yu - yd) + yd
        calDistance = lambda x, y: np.sqrt((x[0] - y[0]) ** 2 + (x[1] - y[1]) ** 2)
    
        points = [(0, 0)] * N
        for i in range(N):
            points[i] = (fx(), fy())
        Mat = np.zeros((N, N))
        for i in range(N):
            for j in range(i + 1, N):
                dv = calDistance(points[i], points[j])
                Mat[i][j], Mat[j][i] = dv, dv
        return points, Mat
    
    
    def calpathValue(path):
        global Mat
        temp = Mat[0][path[0]]
        for i in range(len(path) - 1):
            temp += Mat[path[i]][path[i + 1]]
        temp += Mat[path[-1]][0]
        return temp
    
    
    def initial():
        global N
        init = list(range(1, N, 1))
        random.shuffle(init)
        packValue = calpathValue(init)
        return init, packValue
    
    
    def draw(path, pv):
        global points, N, TIMESIT, PNGFILE, PNGLIST
        plt.cla()
        plt.title('cross=%.4f' % pv)
        xs = [p[0] for p in points]
        ys = [p[1] for p in points]
        plt.scatter(xs, ys, color='b')
        xs = np.array(xs)
        ys = np.array(ys)
        plt.plot(xs[[0, path[0]]], ys[[0, path[0]]], color='r')
        for i in range(N - 2):
            plt.plot(xs[[path[i], path[i + 1]]], ys[[path[i], path[i + 1]]], color='r')
        plt.plot(xs[[path[N - 2], 0]], ys[[path[N - 2], 0]], color='r')
        plt.scatter(xs[0], ys[0], color='k', linewidth=10)
        for i, p in enumerate(points):
            plt.text(*p, '%d' % i)
        plt.savefig('%s/%d.png' % (PNGFILE, TIMESIT))
        PNGLIST.append('%s/%d.png' % (PNGFILE, TIMESIT))
        TIMESIT += 1
    
    
    if __name__ == '__main__':
        # N, Mat = read_data()
        TIMESIT = 0
        PNGFILE = './png/'
        PNGLIST = []
        if not os.path.exists(PNGFILE):
            os.mkdir(PNGFILE)
        else:
            shutil.rmtree(PNGFILE)
            os.mkdir(PNGFILE)
        N = 30
        points, Mat = create_data(N)
    
        T = 1000  # 起始温度
        alpha = 0.995  # T_{k+1} = alpha * T_k方式更新温度
        limitedT = 1.  # 最小值的T
        iterTime = 1000  # 每个温度下迭代的次数
        K = 0.8  # 系数K
        p = 0
        path, value = initial()
        tempPath, tempValue = [], 0
        global_Best = value  # 画图
        while T > limitedT:
            print(T)
            for i in range(iterTime):
                tempPath = path.copy()
                tx = random.randint(0, N - 2)
                ty = random.randint(0, N - 2)
                if tx != ty:
                    tempPath[tx], tempPath[ty] = tempPath[ty], tempPath[tx]
                    tempValue = calpathValue(tempPath)
                    if tempValue <= value:
                        path = tempPath.copy()
                        value = tempValue.copy()
                    else:
                        p = np.exp((value - tempValue) / (K * T))
                        if random.random() < p:
                            path = tempPath.copy()
                            value = tempValue.copy()
            if value < global_Best:
                global_Best = value
                draw(path, value)
            T *= alpha
    
        print(value)
        print(0, end='-->')
        for i in path:
            print(i, end='-->')
    
        generated_images = []
        for png_path in PNGLIST:
            generated_images.append(imageio.imread(png_path))
        shutil.rmtree(PNGFILE)  # 可删掉
        generated_images = generated_images + [generated_images[-1]] * 5
        imageio.mimsave('TSP-SAA.gif', generated_images, 'GIF', duration=0.5)

[Python_C_TSP]: https://blog.csdn.net/a19990412/article/details/84112993
[TSP_ Python_160]: https://blog.csdn.net/a19990412/article/details/84978612
[20181219171938363.gif]: /images/20220404/ec756e83c29d49e2a0227ee8cf12c604.png