参考文档：https://blog.csdn.net/golden1314521/article/details/45059719
简单的修改下，能够在python3.6中运行
代码如下：
import numpy as np import matplotlib.pyplot as plt # %pylab coordinates = np.array([[565.0, 575.0], [25.0, 185.0], [345.0, 750.0], [945.0, 685.0], [845.0, 655.0], [880.0, 660.0], [25.0, 230.0], [525.0, 1000.0], [580.0, 1175.0], [650.0, 1130.0], [1605.0, 620.0], [1220.0, 580.0], [1465.0, 200.0], [1530.0, 5.0], [845.0, 680.0], [725.0, 370.0], [145.0, 665.0], [415.0, 635.0], [510.0, 875.0], [560.0, 365.0], [300.0, 465.0], [520.0, 585.0], [480.0, 415.0], [835.0, 625.0], [975.0, 580.0], [1215.0, 245.0], [1320.0, 315.0], [1250.0, 400.0], [660.0, 180.0], [410.0, 250.0], [420.0, 555.0], [575.0, 665.0], [1150.0, 1160.0], [700.0, 580.0], [685.0, 595.0], [685.0, 610.0], [770.0, 610.0], [795.0, 645.0], [720.0, 635.0], [760.0, 650.0], [475.0, 960.0], [95.0, 260.0], [875.0, 920.0], [700.0, 500.0], [555.0, 815.0], [830.0, 485.0], [1170.0, 65.0], [830.0, 610.0], [605.0, 625.0], [595.0, 360.0], [1340.0, 725.0], [1740.0, 245.0]]) def getdistmat(coordinates): num = coordinates.shape[0] #求第一维的长度 distmat = np.zeros((52, 52)) for i in range(num): for j in range(i, num): distmat[i][j] = distmat[j][i] = np.linalg.norm(coordinates[i]-coordinates[j]) return distmat distmat = getdistmat(coordinates) numant = 40 #蚂蚁个数 numcity = coordinates.shape[0] #城市个数 alpha = 1 #信息素重要程度因子 beta = 5 #启发函数重要程度因子 rho = 0.1 #信息素的挥发速度 Q = 1 iter = 0 itermax = 200 etatable = 1.0/(distmat+np.diag([1e10]*numcity)) #启发函数矩阵，表示蚂蚁从城市i转移到矩阵j的期望程度 pheromonetable = np.ones((numcity, numcity)) # 信息素矩阵 pathtable = np.zeros((numant, numcity)).astype(int) #路径记录表 distmat = getdistmat(coordinates) #城市的距离矩阵 lengthaver = np.zeros(itermax) #各代路径的平均长度 lengthbest = np.zeros(itermax) #各代及其之前遇到的最佳路径长度 pathbest = np.zeros((itermax, numcity)) # 各代及其之前遇到的最佳路径长度 while iter < itermax: # 随机产生各个蚂蚁的起点城市 if numant <= numcity:#城市数比蚂蚁数多 pathtable[:, 0] = np.random.permutation(range(0, numcity))[:numant] else: #蚂蚁数比城市数多，需要补足 pathtable[:numcity, 0] = np.random.permutation(range(0, numcity))[:] pathtable[numcity:, 0] = np.random.permutation(range(0, numcity))[:numant-numcity] length = np.zeros(numant) #计算各个蚂蚁的路径距离 for i in range(numant): visiting = pathtable[i, 0] # 当前所在的城市 #visited = set() #已访问过的城市，防止重复 #visited.add(visiting) #增加元素 unvisited = set(range(numcity))#未访问的城市 unvisited.remove(visiting) #删除元素 for j in range(1,numcity):#循环numcity-1次，访问剩余的numcity-1个城市 #每次用轮盘法选择下一个要访问的城市 listunvisited = list(unvisited) probtrans = np.zeros(len(listunvisited)) for k in range(len(listunvisited)): probtrans[k] = np.power(pheromonetable[visiting][listunvisited[k]], alpha)\ *np.power(etatable[visiting][listunvisited[k]], alpha) cumsumprobtrans = (probtrans/sum(probtrans)).cumsum() cumsumprobtrans -= np.random.rand() for n in range(len(cumsumprobtrans)): if cumsumprobtrans[n] > 0: break k = listunvisited[n] #下一个要访问的城市 pathtable[i, j] = k unvisited.remove(k) #visited.add(k) length[i] += distmat[visiting][k] visiting = k length[i] += distmat[visiting][pathtable[i,0]] #蚂蚁的路径距离包括最后一个城市和第一个城市的距离 #print length # 包含所有蚂蚁的一个迭代结束后，统计本次迭代的若干统计参数 lengthaver[iter] = length.mean() if iter == 0: lengthbest[iter] = length.min() pathbest[iter] = pathtable[length.argmin()].copy() else: if length.min() > lengthbest[iter-1]: lengthbest[iter] = lengthbest[iter-1] pathbest[iter] = pathbest[iter-1].copy() else: lengthbest[iter] = length.min() pathbest[iter] = pathtable[length.argmin()].copy() # 更新信息素 changepheromonetable = np.zeros((numcity, numcity)) for i in range(numant): for j in range(numcity-1): changepheromonetable[pathtable[i, j]][pathtable[i, j+1]] += Q/distmat[pathtable[i, j]][pathtable[i, j+1]] changepheromonetable[pathtable[i, j+1]][pathtable[i, 0]] += Q/distmat[pathtable[i, j+1]][pathtable[i, 0]] pheromonetable = (1-rho)*pheromonetable + changepheromonetable iter += 1 #迭代次数指示器+1 #观察程序执行进度，该功能是非必须的 if (iter-1) % 20 == 0: print(iter-1) print(pathbest) # 做出平均路径长度和最优路径长度 fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(12, 10)) axes[0].plot(lengthaver, 'k', marker = u'') axes[0].set_title('Average Length') axes[0].set_xlabel(u'iteration') axes[1].plot(lengthbest, 'k', marker = u'') axes[1].set_title('Best Length') axes[1].set_xlabel(u'iteration') fig.savefig('Average_Best.png', dpi=500, bbox_inches='tight') plt.close() #作出找到的最优路径图 bestpath = pathbest[-1] plt.plot(coordinates[:, 0], coordinates[:, 1], 'r.', marker=u'$\cdot$') plt.xlim([-100, 2000]) plt.ylim([-100, 1500]) end = 0 for i in range(numcity-1): m, n = int(bestpath[i]), int(bestpath[i+1]) print(m, n) plt.plot([coordinates[m][0], coordinates[n][0]], [coordinates[m][1], coordinates[n][1]], 'k') end = n plt.plot([coordinates[int(bestpath[0])][0], coordinates[end][0]], [coordinates[int(bestpath[0])][1], coordinates[end][1]], 'b') ax = plt.gca() ax.set_title("Best Path") ax.set_xlabel('X axis') ax.set_ylabel('Y_axis') plt.savefig('Best Path3.png', dpi=500, bbox_inches='tight') plt.close()
