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GA.py
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GA.py
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# -*- coding: utf-8 -*-
import random
from TSP_readfile import get_distance
from Genome import Genome
distance,length = get_distance("TSPINF.txt")
itrNum = 100 #for climbing method
INF = 0xfffffff
class GA(object):
"""
种群类
"""
def __init__(self, popsize,chromolength,crossrate,mutationrate):
'''
popSize 人口总数
chromoLength 基因长度
crossRate 交叉概率
mutationRare 变异概率
population 种群列表（存放个体基因）
totalFitness 种群总适应度
generation 代数
bestFiness 最佳适应度
bestGenome 最佳个体基因
'''
self.popSize = popsize
self.chromoLength = chromolength
self.crossRate = crossrate
self.mutationRate = mutationrate
self.generation = 1
self.population = list()
self.totalFitness = 0
self.bestFitness = -1
self.bestGenome = None
self.InitPop()
def InitPop(self): #初始化种群
for i in range(self.popSize):
genome = list()
for j in range(self.chromoLength-1):
j = j + 2
genome.append(j)
random.shuffle(genome)
genome.insert(0,1)
self.population.append(Genome(genome))
def SetPopFitness(self): #获取种群适应度
self.totalFitness = 0
for genome in self.population:
genome.fitness = self.SetFitness(genome.gene)
self.totalFitness += genome.fitness
if genome.fitness > self.bestFitness:
self.bestFitness = genome.fitness
self.bestGenome = genome
def SetFitness(self,genome): #获取个体适应度
global distance
fitness = 0
for i in range(self.chromoLength):
t = genome[i]
k = genome[(i+1)%self.chromoLength]
fitness += distance[t-1][t,k]
return 1.0/fitness
def CrossOver(self,mom,dad):
index1 = random.randint(1,self.chromoLength-1)
index2 = random.randint(index1,self.chromoLength-1)
tmpBaby1 = mom[index1:index2]
tmpBaby2 = dad[index1:index2]
tmpDad = dad[index2:]+dad[1:index1]+tmpBaby2
for i in range(index2-index1):
i = i + index1
tmpDad.remove(mom[i])
newBaby = tmpDad[self.chromoLength-index2:]+tmpBaby1[:] + tmpDad[:self.chromoLength-index2]
newBaby.insert(0,1)
return newBaby
'''
def CrossOver(self,mom,dad):
index = random.randint(1,self.chromoLength-3)
tmpX = mom[index]
newBaby = list()
for i in range(len(dad)):
if dad[i] == tmpX:
tmpY = dad[i-1]
break
if tmpY == mom[index+1] or tmpY==mom[index-1]:
newBaby = mom[:]
else:
newBaby = self.ReserveGenome(mom,index,tmpY)
return newBaby
'''
def Mutate(self,baby):
index1 = random.randint(0,self.chromoLength-1)
index2 = random.randint(index1,self.chromoLength-1)
tmp = baby[index1+1:index2+1]
tmpBaby = baby[:index1+1]+tmp[::-1]+baby[index2+1:]
tmpFitness1 = self.SetFitness(tmpBaby)
tmpFitness2 = self.SetFitness(baby)
if tmpFitness1 > tmpFitness2:
return tmpBaby
return baby
def Tournament_Selection(self):
tmp = random.sample(self.population,2)
if tmp[0].fitness>tmp[1].fitness:
return tmp[0]
else:
return tmp[1]
def ReserveGenome(self,mom,index,tmp):
for i in range(len(mom)):
if mom[i]==tmp:
if i<index:
t = mom[i+1:index+1]
baby = mom[:i+1]+t+mom[index+1:]
else:
t = mom[index+1:i+1]
baby = mom[:index+1]+t+mom[i+1:]
return baby
def ClimbMethod(self,genome,fitness):
bestV = fitness
bestG = genome[:]
for i in range(itrNum):
gen = bestG[:]
index1 = random.randint(1,len(genome)-2)
index2 = random.randint(1,len(genome)-2)
gen[index1],gen[index2] = gen[index2],gen[index1]
tmpFitness = self.SetFitness(gen)
if tmpFitness > bestV:
bestV = tmpFitness
bestG = gen
return bestG
def ClimbMethod1(self,genome,fitness):
bestV = fitness
bestG = genome
for i in range(itrNum):
cnt = 1
gen = bestG.gene[:]
index1 = random.randint(0,len(genome)-3)
tmpA = gen[index1]
tmpB = gen[index1+1]
valAB = distance[tmpA-1][tmpA,tmpB]
while(True):
index2 = random.randint(1,len(genome)-2)
tmpC = gen[index2]
valAC = distance[tmpA-1][tmpA,tmpC]
if valAC<valAB or cnt == 20:
break
cnt += 1
gen[index1+1],gen[index2] = gen[index2],gen[index1+1]
tmpFitness = self.SetFitness(gen)
if tmpFitness > bestV:
bestV = tmpFitness
bestG = Genome(gen,bestV)
return bestG,bestV
def ClimbMethod2(self,genome,fitness):
global INF
bestV = fitness
bestG = genome
for i in range(itrNum):
gen = bestG.gene[:]
index1 = random.randint(1,len(genome)-3)
tmpA = gen[index1]
val = INF
dis_index = distance[tmpA-1]
for i in range(self.chromoLength):
i = i + 1
if val >= dis_index[tmpA,i] and dis_index[tmpA,i] != 0 :
val = dis_index[tmpA,i]
tmpB = i
for i in range(self.chromoLength):
if gen[i] == tmpB:
index2 = i
break
if tmpB == 1 and tmpA != 1:
gen[index1],gen[index2+1] = gen[index2+1],gen[index1]
elif tmpB != 1:
gen[index1+1],gen[index2] = gen[index2],gen[index1+1]
tmpFitness = self.SetFitness(gen)
if tmpFitness > bestV:
bestV = tmpFitness
bestG = Genome(gen,bestV)
return bestG,bestV
'''
def ClimbMethodForPop(self):
for i in range(self.popSize):
self.population[i].gene,self.population[i].fitness = self.ClimbMethod1(self.population[i].gene,self.population[i].fitness)
if self.population[i].fitness > self.bestFitness:
self.bestFitness = self.population[i].fitness
self.bestGenome = self.population[i].gene
'''
def Epoch(self,newPop):
self.population = newPop #使当前种群等于传入种群
self.SetPopFitness() #获取种群适应度
#self.ClimbMethodForPop()
newPop = list() #生成子代容器
self.bestGenome,self.bestFitness = self.ClimbMethod2(self.bestGenome,self.bestFitness)
#对最优个体进行爬山，选取局部最优
#self.bestGenome,self.bestFitness = self.ClimbMethod2(self.bestGenome,self.bestFitness)
newPop.append(self.bestGenome) #压入最优个体
while(len(newPop)<self.popSize):
mom = self.Tournament_Selection() #锦标赛选择
dad = self.Tournament_Selection()
#climbMom = self.ClimbMethod1(mom.gene[:],mom.fitness)
#climbDad = self.ClimbMethod1(dad.gene[:],dad.fitness)
rate = random.random()
if rate < self.crossRate:
#baby = self.CrossOver(climbMom,climbDad)
baby = self.CrossOver(mom.gene[:],dad.gene[:])
else:
#baby = climbMom
baby = mom.gene[:]
if rate <self.mutationRate:
baby = self.Mutate(baby)
newPop.append(Genome(baby)) #压入子代
return newPop