python3 --蚁群算法实现

代码来自https://blog.csdn.net/golden1314521/article/details/45059719

仅做了相关python3代码的修改

希望能帮助到大家

import numpy as np

import 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]
    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 = 250
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
    #print(length)
    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,以集合的形式存储{}
        #print(unvisited)
        unvisited.remove(visiting) #删除元素；利用集合的remove方法删除存储的数据内容
        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()
            #print(cumsumprobtrans)

          # k = listunvisited[str.find(cumsumprobtrans>0)[0]] #下一个要访问的城市
            k=listunvisited[(np.where(cumsumprobtrans>0)[0])[0]]#python3中原代码运行bug，类型问题；鉴于此特找到其他方法
            #通过where（）方法寻找矩阵大于0的元素的索引并返回ndarray类型，然后接着载使用[0]提取其中的元素，用作listunvisited列表中
            #元素的提取（也就是下一轮选的城市）
            #print('k:',k)
            pathtable[i,j] = k#添加到路径表中（也就是蚂蚁走过的路径)

            unvisited.remove(k)#然后在为访问城市set中remove（）删除掉该城市
            #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
        # 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]]#计算信息素增量

        #changepheromonetable[pathtable[i,j+1]][pathtable[i,0]] += Q/distmat[pathtable[i,j+1]][pathtable[i,0]]

    pheromonetable = (1-rho)*pheromonetable + changepheromonetable#计算信息素公式

    iter += 1 #迭代次数指示器+1
    #print("iter:",iter)

    #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:",pathbest)
print("lengthbest:",lengthbest)
#绘制寻优过程曲线图
x=np.array(range(250))
pylab.plot(x,lengthbest)
pylab.show()

python3 --蚁群算法实现

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