对《禁忌搜索(Tabu Search)算法及python实现》的修改

这个算法是在听北大人工智能mooc的时候，老师讲的一种局部搜索算法，可是举得例子不太明白。搜索网页后，发现《禁忌搜索(Tabu Search)算法及python实现》（https://blog.csdn.net/adkjb/article/details/81712969）已经做了好详细的介绍，仔细看了下很有收获。于是想泡泡代码，看前面还好，后边的代码有些看不懂了，而且在函数里定义函数，这种做法少见，并且把函数有当作类来用，为什么不直接用类呢。还有就是，可能对禁忌搜索不太了解，可能具体算法在代码里有问题，欢迎提出。

import random 

class Tabu:
    def __init__(self,tabulen=100,preparelen=200):
        self.tabulen=tabulen
        self.preparelen=preparelen
        self.city,self.cityids,self.stid=self.loadcity2()  #我直接把他的数据放到代码里了
        
        self.route=self.randomroute()
        self.tabu=[]
        self.prepare=[]
        self.curroute=self.route.copy()
        self.bestcost=self.costroad(self.route)
        self.bestroute=self.route
    def loadcity(self,f="d:/Documents/code/aiclass/tsp.txt",stid=1):
        city = {} 
        cityid=[]
                     
        for line in open(f): 
            place,lon,lat = line.strip().split(" ") 
            city[int(place)]=float(lon),float(lat) #导入城市的坐标 
            cityid.append(int(place))
        return city,cityid,stid
    def loadcity2(self,stid=1):
        city={1: (1150.0, 1760.0), 2: (630.0, 1660.0), 3: (40.0, 2090.0), 4: (750.0, 1100.0), 
              5: (750.0, 2030.0), 6: (1030.0, 2070.0), 7: (1650.0, 650.0), 8: (1490.0, 1630.0), 
              9: (790.0, 2260.0), 10: (710.0, 1310.0), 11: (840.0, 550.0), 12: (1170.0, 2300.0), 
              13: (970.0, 1340.0), 14: (510.0, 700.0), 15: (750.0, 900.0), 16: (1280.0, 1200.0), 
              17: (230.0, 590.0), 18: (460.0, 860.0), 19: (1040.0, 950.0), 20: (590.0, 1390.0), 
              21: (830.0, 1770.0), 22: (490.0, 500.0), 23: (1840.0, 1240.0), 24: (1260.0, 1500.0), 
              25: (1280.0, 790.0), 26: (490.0, 2130.0), 27: (1460.0, 1420.0), 28: (1260.0, 1910.0),
              29: (360.0, 1980.0)}  #原博客里的数据
        cityid=list(city.keys())
        return city,cityid,stid
    def costroad(self,road):
        #计算当前路径的长度 与原博客里的函数功能相同
        d=-1
        st=0,0
        cur=0,0
        city=self.city
        for v in road:
            if d==-1:
                st=city[v]
                cur=st
                d=0
            else:
                d+=((cur[0]-city[v][0])**2+(cur[1]-city[v][1])**2)**0.5 #计算所求解的距离，这里为了简单，视作二位平面上的点，使用了欧式距离
                cur=city[v]
        d+=((cur[0]-st[0])**2+(cur[1]-st[1])**2)**0.5
        return d
    def randomroute(self):
        #产生一条随机的路
        stid=self.stid
        rt=self.cityids.copy()
        random.shuffle(rt)
        rt.pop(rt.index(stid))
        rt.insert(0,stid)
        return rt
    def randomswap2(self,route):
        #随机交换路径的两个节点
        route=route.copy()
        while True:
            a=random.choice(route)
            b=random.choice(route)
            if a==b or a==1 or b==1:
                continue
            ia=route.index(a)
            ib=route.index(b)
            route[ia]=b
            route[ib]=a
            return route
    def step(self):
        #搜索一步路找出当前下应该搜寻的下一条路
        rt=self.curroute
        
        i=0
        while i<self.preparelen:     #产生候选路径
            prt=self.randomswap2(rt)
            if int(self.costroad(prt)) not in self.tabu:    #产生不在禁忌表中的路径
                self.prepare.append(prt.copy())
                i+=1
        c=[]
        for r in self.prepare:
            c.append(self.costroad(r))     
        mc=min(c)
        mrt=self.prepare[c.index(mc)]     #选出候选路径里最好的一条
        if mc<self.bestcost:    
            self.bestcost=mc
            self.bestroute=mrt.copy()      #如果他比最好的还要好，那么记录下来
        self.tabu.append(mc)#int(mrt))    #这里本来要加 mrt的 ，可是mrt是路径，要对比起来麻烦，这里假设每条路是由长度决定的
                                    #也就是说 每个路径和他的长度是一一对应，这样比对起来速度快点，当然这样可能出问题，更好的有待研究
        self.curroute=mrt   #用候选里最好的做下次搜索的起点
        self.prepare=[]
        if len(self.tabu)>self.tabulen:
            self.tabu.pop(0)

下面跑跑看：

import timeit
t=Tabu()print('ok')
print(t.city)
print(t.route)
print(t.bestcost)
print(t.curroute)
for i in range(1000):
    t.step()
    if i%50==0:
        print(t.bestcost)
        print(t.bestroute)
        print(t.curroute)

print('ok')
#print(timeit.timeit(stmt="t.step()", number=1000,globals=globals()))
print('ok')

ok
{1: (1150.0, 1760.0), 2: (630.0, 1660.0), 3: (40.0, 2090.0), 4: (750.0, 1100.0), 5: (750.0, 2030.0), 6: (1030.0, 2070.0), 7: (1650.0, 650.0), 8: (1490.0, 1630.0), 9: (790.0, 2260.0), 10: (710.0, 1310.0), 11: (840.0, 550.0), 12: (1170.0, 2300.0), 13: (970.0, 1340.0), 14: (510.0, 700.0), 15: (750.0, 900.0), 16: (1280.0, 1200.0), 17: (230.0, 590.0), 18: (460.0, 860.0), 19: (1040.0, 950.0), 20: (590.0, 1390.0), 21: (830.0, 1770.0), 22: (490.0, 500.0), 23: (1840.0, 1240.0), 24: (1260.0, 1500.0), 25: (1280.0, 790.0), 26: (490.0, 2130.0), 27: (1460.0, 1420.0), 28: (1260.0, 1910.0), 29: (360.0, 1980.0)}
[1, 6, 2, 12, 9, 28, 15, 21, 8, 22, 29, 19, 26, 24, 20, 18, 17, 7, 4, 16, 27, 11, 14, 25, 10, 3, 13, 23, 5]
24651.706120672443
[1, 6, 2, 12, 9, 28, 15, 21, 8, 22, 29, 19, 26, 24, 20, 18, 17, 7, 4, 16, 27, 11, 14, 25, 10, 3, 13, 23, 5]
21567.36269967159
[1, 6, 2, 12, 9, 28, 15, 21, 8, 22, 29, 3, 26, 24, 20, 18, 17, 7, 4, 16, 27, 11, 14, 25, 10, 19, 13, 23, 5]
[1, 6, 2, 12, 9, 28, 15, 21, 8, 22, 29, 3, 26, 24, 20, 18, 17, 7, 4, 16, 27, 11, 14, 25, 10, 19, 13, 23, 5]
9701.867337779715
[1, 28, 6, 12, 9, 5, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
[1, 28, 6, 12, 9, 5, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 8, 27, 23, 7, 25, 15, 4, 13, 24]
9701.867337779715
[1, 28, 6, 12, 9, 5, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
[1, 28, 12, 6, 5, 9, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 16, 27, 8, 23, 7, 25, 19, 4, 13, 24]
9701.867337779715
[1, 28, 6, 12, 9, 5, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
[1, 28, 12, 6, 5, 9, 26, 29, 3, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
9701.867337779715
[1, 28, 6, 12, 9, 5, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
[1, 28, 12, 6, 9, 5, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
9667.242844275002
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
[1, 28, 6, 12, 9, 3, 29, 26, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 16, 8, 27, 23, 7, 25, 19, 4, 13, 24]
9667.242844275002
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
9667.242844275002
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
[1, 28, 6, 12, 9, 5, 26, 29, 3, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
9667.242844275002
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 16, 27, 8, 23, 7, 25, 15, 4, 13, 24]
[1, 28, 6, 12, 9, 5, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 16, 27, 8, 23, 7, 25, 19, 4, 13, 24]
9248.522952771107
[1, 28, 6, 12, 9, 5, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
[1, 28, 12, 6, 9, 5, 29, 3, 26, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
9213.898459266395
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 19, 15, 4, 13, 16, 25, 7, 23, 8, 27, 24]
9213.898459266395
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
[1, 28, 12, 6, 9, 5, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 8, 27, 24]
9213.898459266395
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
[1, 28, 6, 12, 9, 5, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
9213.898459266395
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
[1, 28, 12, 6, 5, 9, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
9213.898459266395
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
[1, 28, 6, 12, 9, 5, 29, 3, 26, 21, 2, 20, 10, 18, 17, 14, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
9213.898459266395
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
[1, 28, 12, 6, 9, 26, 29, 3, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 8, 27, 24]
9213.898459266395
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
[1, 28, 12, 6, 9, 5, 26, 29, 3, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
9213.898459266395
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
[1, 28, 12, 6, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
9213.898459266395
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
[1, 28, 12, 6, 5, 9, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
9213.898459266395
[1, 28, 6, 12, 9, 26, 3, 29, 5, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]
[1, 28, 6, 12, 9, 5, 26, 3, 29, 21, 2, 20, 10, 18, 14, 17, 22, 11, 15, 4, 13, 16, 19, 25, 7, 23, 27, 8, 24]

看到最小路径是9213.89 如果我们把timeit去掉，跑1000步我的电脑是不到4秒大概

为了直观把路径画下来：

from matplotlib import pyplot 

x=[]
y=[]
print("最优路径长度:",t.bestcost)
for i in t.bestroute:
    x0,y0=t.city[i]
    x.append(x0)
    y.append(y0)
x.append(x[0])
y.append(y[0])
pyplot.plot(x,y)
pyplot.scatter(x,y)