python实现遗传算法求解TSP旅行商问题（详细解释）

课设需要做这个题目，看了http://www.tuicool.com/articles/Fb2YjeF之后下了他在github上的代码https://github.com/zchlong/tsp，发现一些错别字这份代码里改正了。

三个文件：

1.GA.py遗传算法类

2.Life.py基因序列类

3.TSP_GA.py 旅行商算法类，届时直接运行这个代码文件即可

4.34所城市经纬度distanceMatrix.txt

格式：

城市名 经度 纬度

1.GA.py遗传算法类

# -*- coding: utf-8 -*-import randomfrom Life import Lifeclass GA(object):      """遗传算法类"""      def __init__(self, aCrossRate, aMutationRate, aLifeCount, aGeneLength, aMatchFun = lambda life : 1):            self.crossRate = aCrossRate               #交叉概率            self.mutationRate = aMutationRate         #突变概率            self.lifeCount = aLifeCount               #种群数量，就是每次我们在多少个城市序列里筛选，这里初始化为100            self.geneLength = aGeneLength             #其实就是城市数量            self.matchFun = aMatchFun                 #适配函数            self.lives = []                           #种群            self.best = None                          #保存这一代中最好的个体            self.generation = 1                       #一开始的是第一代            self.crossCount = 0                       #一开始还没交叉过，所以交叉次数是0            self.mutationCount = 0                    #一开始还没变异过，所以变异次数是0            self.bounds = 0.0                         #适配值之和，用于选择时计算概率            self.initPopulation()                     #初始化种群      def initPopulation(self):            """初始化种群"""            self.lives = []            for i in range(self.lifeCount):                  #gene = [0,1,…… ,self.geneLength-1]                  #事实就是0到33                  gene = range(self.geneLength)                  #将0到33序列的所有元素随机排序得到一个新的序列                  random.shuffle(gene)                  #Life这个类就是一个基因序列，初始化life的时候,两个参数，一个是序列gene，一个是这个序列的初始适应度值                  # 因为适应度值越大，越可能被选择，所以一开始种群里的所有基因都被初始化为-1                  life = Life(gene)                  #把生成的这个基因序列life填进种群集合里                  self.lives.append(life)      def judge(self):            """评估，计算每一个个体的适配值"""            # 适配值之和，用于选择时计算概率            self.bounds = 0.0            #假设种群中的第一个基因被选中            self.best = self.lives[0]            for life in self.lives:                  life.score = self.matchFun(life)                  self.bounds += life.score                  #如果新基因的适配值大于原先的best基因，就更新best基因                  if self.best.score < life.score:                        self.best = life      def cross(self, parent1, parent2):            """交叉"""            index1 = random.randint(0, self.geneLength - 1)            index2 = random.randint(index1, self.geneLength - 1)            tempGene = parent2.gene[index1:index2]                      #交叉的基因片段            newGene = []            p1len = 0            for g in parent1.gene:                  if p1len == index1:                        newGene.extend(tempGene)                               #插入基因片段                        p1len += 1                  if g not in tempGene:                        newGene.append(g)                        p1len += 1            self.crossCount += 1            return newGene      def  mutation(self, gene):            """突变"""            #相当于取得0到self.geneLength - 1之间的一个数，包括0和self.geneLength - 1            index1 = random.randint(0, self.geneLength - 1)            index2 = random.randint(0, self.geneLength - 1)            #把这两个位置的城市互换            gene[index1], gene[index2] = gene[index2], gene[index1]            #突变次数加1            self.mutationCount += 1            return gene      def getOne(self):            """选择一个个体"""            #产生0到（适配值之和）之间的任何一个实数            r = random.uniform(0, self.bounds)            for life in self.lives:                  r -= life.score                  if r <= 0:                        return life            raise Exception("选择错误", self.bounds)      def newChild(self):            """产生新后的"""            parent1 = self.getOne()            rate = random.random()            #按概率交叉            if rate < self.crossRate:                  #交叉                  parent2 = self.getOne()                  gene = self.cross(parent1, parent2)            else:                  gene = parent1.gene            #按概率突变            rate = random.random()            if rate < self.mutationRate:                  gene = self.mutation(gene)            return Life(gene)      def next(self):            """产生下一代"""            self.judge()#评估，计算每一个个体的适配值            newLives = []            newLives.append(self.best)#把最好的个体加入下一代            while len(newLives) < self.lifeCount:                  newLives.append(self.newChild())            self.lives = newLives            self.generation += 1

2.Life.py基因序列类

# -*- encoding: utf-8 -*-SCORE_NONE = -1class Life(object):      """个体类"""      def __init__(self, aGene = None):            self.gene = aGene            self.score = SCORE_NONE

3.TSP_GA.py 旅行商算法类，届时直接运行这个代码文件即可

# -*- encoding: utf-8 -*-import randomimport mathimport Tkinterfrom GA import GAclass TSP(object):      def __init__(self, aLifeCount = 100,):            self.initCitys()            self.lifeCount = aLifeCount            self.ga = GA(aCrossRate = 0.7,                   aMutationRate = 0.02,                  aLifeCount = self.lifeCount,                   aGeneLength = len(self.citys),                  aMatchFun = self.matchFun())      def initCitys(self):            self.citys = []            #这个文件里是34个城市的经纬度            f=open("distanceMatrix.txt","r")            while True:                  #一行一行读取                  loci = str(f.readline())                  if loci:                        pass  # do something here                  else:                        break                  #用readline读取末尾总会有一个回车，用replace函数删除这个回车                  loci = loci.replace("\n", "")                  #按照tab键分割                  loci=loci.split("\t")                  # 中国34城市经纬度读入citys                  self.citys.append((float(loci[1]),float(loci[2]),loci[0]))      #order是遍历所有城市的一组序列，如[1,2,3,7,6,5,4,8……]      #distance就是计算这样走要走多长的路      def distance(self, order):            distance = 0.0            #i从-1到32,-1是倒数第一个            for i in range(-1, len(self.citys) - 1):                  index1, index2 = order[i], order[i + 1]                  city1, city2 = self.citys[index1], self.citys[index2]                  distance += math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2)            return distance      #适应度函数，因为我们要从种群中挑选距离最短的，作为最优解，所以（1/距离）最长的就是我们要求的      def matchFun(self):            return lambda life: 1.0 / self.distance(life.gene)      def run(self, n = 0):            while n > 0:                  self.ga.next()                  distance = self.distance(self.ga.best.gene)                  print (("%d : %f") % (self.ga.generation, distance))                  print self.ga.best.gene                  n -= 1            print "经过%d次迭代，最优解距离为：%f"%(self.ga.generation, distance)            print "遍历城市顺序为：",            # print "遍历城市顺序为：", self.ga.best.gene            #打印出我们挑选出的这个序列中            for i in self.ga.best.gene:                  print self.citys[i][2],def main():      tsp = TSP()      tsp.run(100)if __name__ == '__main__':      main()

4.34所城市经纬度distanceMatrix.txt

北京116.4639.92天津117.239.13上海121.4831.22重庆106.5429.59拉萨91.1129.97乌鲁木齐87.6843.77银川106.2738.47呼和浩特111.6540.82南宁108.3322.84哈尔滨126.6345.75长春125.3543.88沈阳123.3841.8石家庄114.4838.03太原112.5337.87西宁101.7436.56济南11736.65郑州113.634.76南京118.7832.04合肥117.2731.86杭州120.1930.26福州119.326.08南昌115.8928.68长沙11328.21武汉114.3130.52广州113.2323.16台北121.525.05海口110.3520.02兰州103.7336.03西安108.9534.27成都104.0630.67贵阳106.7126.57昆明102.7325.04香港114.122.2澳门113.3322.13

参考文献

https://blog.oldj.net/2010/05/22/ga-tsp/

http://www.tuicool.com/articles/Fb2YjeF