def Crossover():
global funEval
for i in range(0,popSize):
if (random.random() <= CR):
# Choose two random indices
i1,i2=random.sample(range(0,popSize), 2)
# Parents
p1=deepcopy(pop[i1])
p2=deepcopy(pop[i2])
# Choose a crossover point
pt = 0
# Generate new childs
c1=p1.chromosome[0:pt] + p2.chromosome[pt:]
c2=p2.chromosome[0:pt] + p1.chromosome[pt:]
# Get the fitness of childs
c1Fitness=ff.FitnessFunction(c1)
funEval = funEval + 1
c2Fitness=ff.FitnessFunction(c2)
funEval = funEval + 1
# Select between parent and child
if c1Fitness < p1.fitness:
pop[i1].fitness=c1Fitness
pop[i1].chromosome=c1
if c2Fitness < p2.fitness:
pop[i2].fitness=c2Fitness
pop[i2].chromosome=c2
def Mutation():
global UB, LB, funEval
for i in range(0,popSize):
if (random.random() <= MR):
# Choose random index
r=random.randint(0,popSize-1)
# Choose a parent
p=deepcopy(pop[r])
# Choose mutation point
pt = random.randint(0,ff.D-1)
# Generate new childs
c=deepcopy(p.chromosome)
# Mutation
c[pt] = round(random.uniform(ff.LB,ff.UB),2)
#Get the fitness of childs
cFitness=ff.FitnessFunction(c)
funEval = funEval + 1
# Select between parent and child
if cFitness < p.fitness:
pop[r].fitness=cFitness
pop[r].chromosome=c
def DEOperation():
global funEval
for i in range(0,popSize):
# Choose three random indices
i1,i2,i3=random.sample(range(0,popSize), 3)
# Iterate for every Dimension
newChild=[]
for j in range(ff.D):
if (random.random() <= CR):
k = pop[i1].chromosome[j] + \
F * (pop[i2].chromosome[j] - pop[i3].chromosome[j])
# If new dimention cross LB
if k < ff.LB:
k = random.uniform(ff.LB,ff.UB)
# If new dimention cross LB
if k > ff.UB:
k = random.uniform(ff.LB,ff.UB)
newChild.append(round(k,2))
else:
newChild.append(pop[i].chromosome[j])
# Child Fitness
newChildFitness=ff.FitnessFunction(newChild)
funEval = funEval + 1
# Select between parent and child
if newChildFitness < pop[i].fitness:
pop[i].fitness=newChildFitness
pop[i].chromosome=newChild
def PSOOperation():
for i in range(0, popSize):
for j in range(0, ff.D):
# Choose two random numbers
r1 = random.random()
r2 = random.random()
# Velocity update
pop[i].velocity[j] = w * pop[i].velocity[j] + \
c1 * r1 * (pop[i].pBest[j] - pop[i].particle[j]) + \
c2 * r2 * (gBest[j] - pop[i].particle[j])
if pop[i].velocity[j] < vLB:
pop[i].velocity[j] = random.uniform(vLB, vUB)
if pop[i].velocity[j] > vUB:
pop[i].velocity[j] = random.uniform(vLB, vUB)
# Particle update
pop[i].particle[j] = round(pop[i].particle[j] + pop[i].velocity[j],
2)
if pop[i].particle[j] < ff.LB:
pop[i].particle[j] = round(random.uniform(ff.LB, ff.UB), 2)
if pop[i].particle[j] > ff.UB:
pop[i].particle[j] = round(random.uniform(ff.LB, ff.UB), 2)
pop[i].particleFitness = round(ff.FitnessFunction(pop[i].particle), 2)
# Select between particle and pBest
if pop[i].particleFitness <= pop[i].pBestFitness:
pop[i].pBest = pop[i].particle
pop[i].pBestFitness = pop[i].particleFitness
def Init():
for i in range(0, popSize):
chromosome = []
for j in range(0, ff.D):
chromosome.append(round(random.uniform(ff.LB, ff.UB), 2))
fitness = ff.FitnessFunction(chromosome)
newIndividual = Individual(chromosome, fitness)
pop.append(newIndividual)
def Init():
global funEval
for i in range(0, popSize):
chromosome = []
chromosome.append(random.uniform(ff.LB, 0))
chromosome.append(random.uniform(0, ff.UB))
fitness = ff.FitnessFunction(chromosome)
funEval = funEval + 1
newIndividual = Individual(chromosome, fitness)
pop.append(newIndividual)
def Init():
for i in range(0, popSize):
particle = []
velocity = []
for j in range(0, ff.D):
particle.append(round(random.uniform(ff.LB, ff.UB), 2))
velocity.append(round(random.uniform(vLB, vUB), 2))
particleFitness = round(ff.FitnessFunction(particle), 2)
newIndividual = Individual(particle, particleFitness, velocity,
deepcopy(particle), particleFitness)
pop.append(newIndividual)
def PSOOperation(pop,funEval,popSize,w,c1,c2,vLB,vUB,gBest,gBestFitness):
##global funEval
#print("h=",gBestFitness)
for i in range(0,popSize):
for j in range(0,ff.D):
# Choose two random numbers
r1=random.random()
r2=random.random()
##if (i%10==0,j==1):
##print("in pso : i ",i," j ",j)
# Velocity update
pop[i].velocity[j] = w * pop[i].velocity[j] + \
c1 * r1 * (pop[i].pBest[j] - pop[i].particle[j]) + \
c2 * r2 * (gBest[j] - pop[i].particle[j])
if pop[i].velocity[j] < vLB:
pop[i].velocity[j] = random.uniform(vLB, vUB)
if pop[i].velocity[j] > vUB:
pop[i].velocity[j] = random.uniform(vLB, vUB)
# Particle update
pop[i].particle[j] = round(pop[i].particle[j] + pop[i].velocity[j],2)
if pop[i].particle[j] < ff.LB:
pop[i].particle[j] = round(random.uniform(ff.LB, ff.UB),2)
if pop[i].particle[j] > ff.UB:
pop[i].particle[j] = round(random.uniform(ff.LB, ff.UB),2)
pop[i].particleFitness = round(ff.FitnessFunction(pop[i].particle),2)
funEval = funEval + 1
# Select between particle and pBest
if pop[i].particleFitness <= pop[i].pBestFitness:
pop[i].pBest=pop[i].particle
pop[i].pBestFitness=pop[i].particleFitness
for p in pop:
#print(p.pBestFitness)
if p.pBestFitness < gBestFitness:
gBest = deepcopy(p.pBest)
gBestFitness=p.pBestFitness
#print("m = ",gBestFitness)
return gBest,gBestFitness
#-------------------------------------------------------------
# Step 5: Start Program
#-------------------------------------------------------------
# Saving Result
fp=open(AlgoName+"Result.csv","w")
fp.write("Iteration,Fitness,Chromosome\n")
for i in range(0,Iterations):
Chromosome = random.sample(,ff.D)
Fitness = ff.FitnessFunction(Chromosome)
if Fitness < BestFitness:
BestFitness=Fitness
BestChromosome=Chromosome
if i%10==0:
print ("I:",i,"\t Fitness:",BestFitness)
fp.write(str(i) + "," + str(BestFitness)+ "," + str(BestChromosome) + "\n")
fp.close()
print ("Done")
print ("\nBest Chromosome:", BestChromosome, "\tFitness:", BestFitness)
print ("Result is saved in", AlgoName+"Result.csv")
print ("Total Time Taken: ", round(time.time() - startTime,2), " sec\n")
