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 = Pop[i1]
p2 = Pop[i2]
# Choose a crossover point
pt = random.randint(1, FF.D - 2)
# 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.MyFitFun(q, c1)
FunEval = FunEval + 1
c2Fitness = FF.MyFitFun(q, 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 FunEval, q
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.MyFitFun(q, 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_Inertia * (
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 = round(FF.MyFitFun(q, NewChild), 2)
FunEval = FunEval + 1
# Select between parent and child
if NewChildFitness < Pop[i].Fitness:
Pop[i].Fitness = NewChildFitness
Pop[i].Chromosome = NewChild
def Init():
global FunEval
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.MyFitFun(q, Chromosome)
FunEval = FunEval + 1
NewIndividual = Individual(Chromosome, Fitness)
Pop.append(NewIndividual)
def Init():
global funEval
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.MyFitFun(q, particle), 2)
funEval = funEval + 1
newIndividual = Individual(particle, particleFitness, velocity,
deepcopy(particle), particleFitness)
Pop.append(newIndividual)
def PSOOperation():
global funEval
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] = Pop[i].particle[j] + Pop[i].velocity[j]
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.MyFitFun(q, Pop[i].particle), 2)
funEval = funEval + 1
# Select between particle and pBest
if Pop[i].particleFitness <= Pop[i].pBestFitness:
Pop[i].pBest = deepcopy(Pop[i].particle)
Pop[i].pBestFitness = deepcopy(Pop[i].particleFitness)
# Step 5: Start Program
#-------------------------------------------------------------
for k in range(Runs): # Multiple Runs of DE
BestFitness = 9999999999 # Store Best Fitness Value
BestChromosome = [] # Store Best Chromosome
R = [] # No of iterations for a particular Run
F = [] # Fitness Obtained in each iteration for a particular Run
FunEval = 0
for i in range(0, Iterations):
Chromosome = []
for j in range(0, FF.D):
Chromosome.append(round(random.uniform(FF.LB, FF.UB), 2))
Fitness = round(FF.MyFitFun(q, Chromosome), 2)
FunEval = FunEval + 1
if Fitness < BestFitness:
BestFitness = Fitness
BestChromosome = Chromosome
R.append(i)
F.append(BestFitness)
Chromos.append(BestChromosome)
Buffer.append(BestFitness)
Fitness_Random.append(F)
bestI = 0 # Index for the best run
for m in range(Runs): # Finding Best Run
if Buffer[bestI] > Buffer[m]:
bestI = m
Chromos.append(GlobalBest.Chromosome)
Buffer.append(GlobalBest.Fitness)
Fitness_GA.append(F)
bestI = 0 # Index for the best run
for m in range(Runs): # Finding Best Run
if Buffer[bestI] > Buffer[m]:
bestI = m
BestFitnessConv = Fitness_GA[
bestI] # Best Fitness List in different iterations for the best Run
Runs_GA = R
GlobalBest.Chromosome = Chromos[bestI]
GlobalBest.Fitness = round(FF.MyFitFun(q, GlobalBest.Chromosome), 2)
#-------------------------------------------------------------
# Step 6: Writing fitnesses obtaine for the best Run
#-------------------------------------------------------------
fp = open('Result' + AlgoName + '.csv', 'w')
fp.write("Iteration,Fitness\n")
for m in range(Iterations):
fp.write(str(m) + ',' + str(BestFitnessConv[m]) + '\n')
fp.close()
print "***********************************************************************************"
print "Genetic"
print "Buffer"
print Buffer
print "\nBest Chromosome:", GlobalBest.Chromosome, "\nBest Fitness:", GlobalBest.Fitness
Chromos.append(gBest)
Buffer.append(gBestFitness)
Fitness_PSO.append(F)
bestI = 0 # Index for the best run
for m in range(Runs): # Finding Best Run
if Buffer[bestI] > Buffer[m]:
bestI = m
BestFitnessConv = Fitness_PSO[
bestI] # Best Fitness List in different iterations for the best Run
Runs_PSO = R
gBest = Chromos[bestI]
gBestFitness = round(FF.MyFitFun(q, gBest), 2)
#-------------------------------------------------------------
# Step 6: Writing fitnesses obtaine for the best Run
#-------------------------------------------------------------
fp = open('Result' + AlgoName + '.csv', 'w')
fp.write("Iteration,Fitness\n")
for m in range(iterations):
fp.write(str(m) + ',' + str(BestFitnessConv[m]) + '\n')
fp.close()
print "***********************************************************************************"
print "PSO"
print "Buffer"
print Buffer
print "Best particle:", gBest
