def ga(self, population, gaFilePath):
population.creatPopulation() # 生成种群
points = []
painter = Painter()
painter.paintNetworkTopology(population.baseRadius,
population.locationOfBase,
population.locationOfUser,
population.basevisitedUE, gaFilePath)
'''存储结果，用于绘图，文件操作'''
f = open(gaFilePath, 'w')
iterations = 1 # 当前迭代次数
while (iterations <= population.iterations):
fitness = population.getAllFitnessIntegral()
maxFitnessInCurrentPopulation = max(fitness)
print("代数：" + str(iterations) + " 最好值：" +
str(maxFitnessInCurrentPopulation))
f.write("iterations: " + str(iterations) + " fitness: " +
str(maxFitnessInCurrentPopulation) + '\n')
if iterations == 1:
maxFitness = maxFitnessInCurrentPopulation
else:
maxFitness = maxFitness if maxFitness > maxFitnessInCurrentPopulation else maxFitnessInCurrentPopulation
points.append([iterations, maxFitnessInCurrentPopulation])
tempIndividulalList = []
while (len(tempIndividulalList) < population.sizeOfPopulation):
individualForCrossover = population.select() # 选择--锦标赛选择法
if len(individualForCrossover) != 2:
print("select error! " + str(len(individualForCrossover)))
exit(1)
individualForMutate = population.crossover(
individualForCrossover) # 交叉随机点位的交叉操作，交叉完毕之后判断是否满足约束
if (len(individualForMutate) == 2):
population.mutate(individualForCrossover)
for i in range(len(individualForCrossover)):
tempIndividulalList.append(individualForCrossover[i])
tempFitness = []
for i in range(len(tempIndividulalList)):
fit = tempIndividulalList[i].getFitnessOfMatching()
tempFitness.append(fit)
for i in range(population.sizeOfPopulation):
if tempIndividulalList == None:
print(str(i) + "个体是空！")
exit(1)
if (tempFitness[i] > fitness[i]):
population.individualList[i] = copy.deepcopy(
tempIndividulalList[i])
iterations += 1
fitness = population.getAllFitnessIntegral()
index = Utils.getImportant(fitness)
print(
"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%9090909090"
)
print(population.individualList[index].getFitnessOfMatching())
print("asm_array: " + str(population.individualList[index].asm_array))
print("ansArray: " + str(population.individualList[index].ansArray))
print("VN： " + str(population.VN))
print("C: " + str(population.individualList[index].C))
print("P: " + str(population.individualList[index].P))
f.write("end" + '\n' + "C: " +
str(population.individualList[index].C) + '\n' + "P: " +
str(population.individualList[index].P))
return [
population.individualList[index].C,
population.individualList[index].P, fitness[index],
population.individualList[index].ansArray
]
def ga(population, fileName, maxFitnessFile, typeOfVQD):
population.initializationOfVQD(typeOfVQD)
population.creatPopulation() # 生成种群
print(str(fileName) + " VQD: " + str(population.VQD))
print("typeOfVQD: " + str(typeOfVQD))
# 存储结果，用于绘图
points = []
maxFitness = -100
minFitness = -100
f = open(fileName, 'w')
f.write(fileName + '\n')
m = open(maxFitnessFile, 'w')
m.write(maxFitnessFile + '\n')
painter = Painter()
painter.paintNetworkTopology(population.baseRadius,
population.locationOfBase,
population.locationOfUser,
population.basevisitedUE, maxFitnessFile)
'''存储结果，用于绘图，文件操作'''
iterations = 1 # 当前迭代次数
while (iterations <= population.iterations):
fitness = population.getAllFitnessIntegral()
maxFitness = max(maxFitness, max(fitness))
minFitness = min(minFitness, min(fitness))
print("代数：" + str(iterations) + " 最好值：" +
str(maxFitness / population.VNTimes))
m.write("iterations " + str(iterations) + ": " +
str(maxFitness / population.VNTimes) + '\n')
f.write("iterations " + str(iterations + 1) + '\n')
f.write(str(fitness) + '\n')
points.append([iterations, maxFitness])
tempIndividulalList = []
while (len(tempIndividulalList) < population.sizeOfPopulation):
individualForCrossover = population.select() # 选择--锦标赛选择法
if len(individualForCrossover) != 2:
print("select error! " + str(len(individualForCrossover)))
exit(1)
individualForMutate = population.crossover(
individualForCrossover) # 交叉随机点位的交叉操作，交叉完毕之后判断是否满足约束
if (len(individualForMutate) == 2):
population.mutate(individualForCrossover)
for i in range(len(individualForCrossover)):
tempIndividulalList.append(individualForCrossover[i])
tempFitness = []
for i in range(len(tempIndividulalList)):
fit = tempIndividulalList[i].getFitnessWithIntegral()
tempFitness.append(fit)
for i in range(population.sizeOfPopulation):
if tempIndividulalList == None:
print(str(i) + "个体是空！")
exit(1)
if (tempFitness[i] > fitness[i]):
population.individualList[i] = copy.deepcopy(
tempIndividulalList[i])
iterations += 1
m.write(str(points) + '\n')
f.close()
m.close()
# 返回画图所需的数据
result = Result(fileName, minFitness, points, maxFitness)
return result
