# PSO parameters

# dim=30

# iters=200

Vmax=6

# PopSize=50 #population size

wMax=0.9

wMin=0.2

c1=2

c2=2

# lb=-10

# ub=10

#

s=solution()

######################## Initializations

vel=numpy.zeros((PopSize,dim))

pBestScore=numpy.zeros(PopSize)

pBestScore.fill(float("inf"))

pBest=numpy.zeros((PopSize,dim))

gBest=numpy.zeros(dim)

gBestScore=float("inf")

pos=numpy.random.uniform(0,1,(PopSize,dim)) *(ub-lb)+lb

convergence_curve=numpy.zeros(iters)

############################################

print("PSO is optimizing \""+objf.__name__+"\"")

timerStart=time.time()

s.startTime=time.strftime("%Y-%m-%d-%H-%M-%S")

for l in range(0,iters):

for i in range(0,PopSize):

#pos[i,:]=checkBounds(pos[i,:],lb,ub)

pos[i,:]=numpy.clip(pos[i,:], lb, ub)

#Calculate objective function for each particle

fitness=objf(pos[i,:])

if(pBestScore[i]>fitness):

pBestScore[i]=fitness

pBest[i,:]=pos[i,:]

if(gBestScore>fitness):

gBestScore=fitness

gBest=pos[i,:]

#Update the W of PSO

w=wMax-l*((wMax-wMin)/iters);

for i in range(0,PopSize):

for j in range (0,dim):

r1=random.random()

r2=random.random()

vel[i,j]=w*vel[i,j]+c1*r1*(pBest[i,j]-pos[i,j])+c2*r2*(gBest[j]-pos[i,j])

if(vel[i,j]>Vmax):

vel[i,j]=Vmax

if(vel[i,j]<-Vmax):

vel[i,j]=-Vmax

pos[i,j]=pos[i,j]+vel[i,j]

convergence_curve[l]=gBestScore

if (l%1==0):

print(['At iteration '+ str(l+1)+ ' the best fitness is '+ str(gBestScore)]);

timerEnd=time.time()

s.endTime=time.strftime("%Y-%m-%d-%H-%M-%S")

s.executionTime=timerEnd-timerStart

s.convergence=convergence_curve

s.optimizer="PSO"

s.objfname=objf.__name__