python实现人工蜂群算法

2023-07-16 11:51:04 63

ABSIndividual.py

importnumpyasnp
importObjFunction


classABSIndividual:

'''
individualofartificialbeeswarmalgorithm
'''

def__init__(self,vardim,bound):
'''
vardim:dimensionofvariables
bound:boundariesofvariables
'''
self.vardim=vardim
self.bound=bound
self.fitness=0.
self.trials=0

defgenerate(self):
'''
generatearandomchromsomeforartificialbeeswarmalgorithm
'''
len=self.vardim
rnd=np.random.random(size=len)
self.chrom=np.zeros(len)
foriinxrange(0,len):
self.chrom[i]=self.bound[0,i]+\
(self.bound[1,i]-self.bound[0,i])*rnd[i]

defcalculateFitness(self):
'''
calculatethefitnessofthechromsome
'''
self.fitness=ObjFunction.GrieFunc(
self.vardim,self.chrom,self.bound)

ABS.py

importnumpyasnp
fromABSIndividualimportABSIndividual
importrandom
importcopy
importmatplotlib.pyplotasplt


classArtificialBeeSwarm:

'''
theclassforartificialbeeswarmalgorithm
'''

def__init__(self,sizepop,vardim,bound,MAXGEN,params):
'''
sizepop:populationsizepop
vardim:dimensionofvariables
bound:boundariesofvariables
MAXGEN:terminationcondition
params:algorithmrequiredparameters,itisalistwhichisconsistingof[trailLimit,C]
'''
self.sizepop=sizepop
self.vardim=vardim
self.bound=bound
self.foodSource=self.sizepop/2
self.MAXGEN=MAXGEN
self.params=params
self.population=[]
self.fitness=np.zeros((self.sizepop,1))
self.trace=np.zeros((self.MAXGEN,2))

definitialize(self):
'''
initializethepopulationofabs
'''
foriinxrange(0,self.foodSource):
ind=ABSIndividual(self.vardim,self.bound)
ind.generate()
self.population.append(ind)

defevaluation(self):
'''
evaluationthefitnessofthepopulation
'''
foriinxrange(0,self.foodSource):
self.population[i].calculateFitness()
self.fitness[i]=self.population[i].fitness

defemployedBeePhase(self):
'''
employedbeephase
'''
foriinxrange(0,self.foodSource):
k=np.random.random_integers(0,self.vardim-1)
j=np.random.random_integers(0,self.foodSource-1)
whilej==i:
j=np.random.random_integers(0,self.foodSource-1)
vi=copy.deepcopy(self.population[i])
#vi.chrom=vi.chrom+np.random.uniform(-1,1,self.vardim)*(
#vi.chrom-self.population[j].chrom)+np.random.uniform(0.0,self.params[1],self.vardim)*(self.best.chrom-vi.chrom)
#forkinxrange(0,self.vardim):
#ifvi.chrom[k]self.bound[1,k]:
#vi.chrom[k]=self.bound[1,k]
vi.chrom[
k]+=np.random.uniform(low=-1,high=1.0,size=1)*(vi.chrom[k]-self.population[j].chrom[k])
ifvi.chrom[k]self.bound[1,k]:
vi.chrom[k]=self.bound[1,k]
vi.calculateFitness()
ifvi.fitness>self.fitness[fi]:
self.population[fi]=vi
self.fitness[fi]=vi.fitness
ifvi.fitness>self.best.fitness:
self.best=vi
vi.calculateFitness()
ifvi.fitness>self.fitness[i]:
self.population[i]=vi
self.fitness[i]=vi.fitness
ifvi.fitness>self.best.fitness:
self.best=vi
else:
self.population[i].trials+=1

defonlookerBeePhase(self):
'''
onlookerbeephase
'''
accuFitness=np.zeros((self.foodSource,1))
maxFitness=np.max(self.fitness)

foriinxrange(0,self.foodSource):
accuFitness[i]=0.9*self.fitness[i]/maxFitness+0.1

foriinxrange(0,self.foodSource):
forfiinxrange(0,self.foodSource):
r=random.random()
ifrself.bound[1,k]:
#vi.chrom[k]=self.bound[1,k]
vi.chrom[
k]+=np.random.uniform(low=-1,high=1.0,size=1)*(vi.chrom[k]-self.population[j].chrom[k])
ifvi.chrom[k]self.bound[1,k]:
vi.chrom[k]=self.bound[1,k]
vi.calculateFitness()
ifvi.fitness>self.fitness[fi]:
self.population[fi]=vi
self.fitness[fi]=vi.fitness
ifvi.fitness>self.best.fitness:
self.best=vi
else:
self.population[fi].trials+=1
break

defscoutBeePhase(self):
'''
scoutbeephase
'''
foriinxrange(0,self.foodSource):
ifself.population[i].trials>self.params[0]:
self.population[i].generate()
self.population[i].trials=0
self.population[i].calculateFitness()
self.fitness[i]=self.population[i].fitness

defsolve(self):
'''
theevolutionprocessoftheabsalgorithm
'''
self.t=0
self.initialize()
self.evaluation()
best=np.max(self.fitness)
bestIndex=np.argmax(self.fitness)
self.best=copy.deepcopy(self.population[bestIndex])
self.avefitness=np.mean(self.fitness)
self.trace[self.t,0]=(1-self.best.fitness)/self.best.fitness
self.trace[self.t,1]=(1-self.avefitness)/self.avefitness
print("Generation%d:optimalfunctionvalueis:%f;averagefunctionvalueis%f"%(
self.t,self.trace[self.t,0],self.trace[self.t,1]))
whileself.tself.best.fitness:
self.best=copy.deepcopy(self.population[bestIndex])
self.avefitness=np.mean(self.fitness)
self.trace[self.t,0]=(1-self.best.fitness)/self.best.fitness
self.trace[self.t,1]=(1-self.avefitness)/self.avefitness
print("Generation%d:optimalfunctionvalueis:%f;averagefunctionvalueis%f"%(
self.t,self.trace[self.t,0],self.trace[self.t,1]))
print("Optimalfunctionvalueis:%f;"%self.trace[self.t,0])
print"Optimalsolutionis:"
printself.best.chrom
self.printResult()

defprintResult(self):
'''
plottheresultofabsalgorithm
'''
x=np.arange(0,self.MAXGEN)
y1=self.trace[:,0]
y2=self.trace[:,1]
plt.plot(x,y1,'r',label='optimalvalue')
plt.plot(x,y2,'g',label='averagevalue')
plt.xlabel("Iteration")
plt.ylabel("functionvalue")
plt.title("ArtificialBeeSwarmalgorithmforfunctionoptimization")
plt.legend()
plt.show()

运行程序：

if__name__=="__main__":

bound=np.tile([[-600],[600]],25)
abs=ABS(60,25,bound,1000,[100,0.5])
abs.solve()

ObjFunction见简单遗传算法-python实现。

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python实现人工蜂群算法

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