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GWO.py
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GWO.py
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# -*- coding: utf-8 -*-
import random
import numpy
from solution import solution
import math
import time
from MultiKernelSVM import *
def GWO(lb,ub,dim,SearchAgents_no,Max_iter):
# initialize alpha, beta, and delta_pos
Alpha_pos=numpy.zeros(dim)
Alpha_score=float("-inf")
Beta_pos=numpy.zeros(dim)
Beta_score=float("-inf")
Delta_pos=numpy.zeros(dim)
Delta_score=float("-inf")
#Initialize the positions of search agents
Positions=numpy.random.uniform(0,1,(SearchAgents_no,dim)) *(ub-lb)+lb
Convergence_curve=numpy.zeros(Max_iter)
s=solution()
# Loop counter
#print("GWO is optimizing \""+objf.__name__+"\"")
timerStart=time.time()
s.startTime=time.strftime("%Y-%m-%d-%H-%M-%S")
z = MultiKernelSVM()
z.preprocessing()
pos1=0;
# Main loop
a_max = 2
a_min = 2-(Max_iter-1)*((2)/(Max_iter))
for l in range(0,Max_iter):
for i in range(0,SearchAgents_no):
Positions = binerization(Positions)
# Return back the search agents that go beyond the boundaries of the search space
# Positions[i,:]=numpy.clip(Positions[i,:], lb, ub)
# Calculate objective function for each search agent
fitness=z.fit(Positions[i,:])
# Update Alpha, Beta, and Delta
if fitness>Alpha_score :
Alpha_score=fitness; # Update alpha
Alpha_pos = assignValue(Positions[i,:])
if (fitness<Alpha_score and fitness>Beta_score ):
Beta_score=fitness # Update beta
Beta_pos=assignValue(Positions[i,:])
if (fitness<Alpha_score and fitness<Beta_score and fitness>Delta_score):
Delta_score=fitness # Update delta
Delta_pos=assignValue(Positions[i,:])
# a=2-l*((2)/Max_iter); # a decreases linearly fron 2 to 0
ln = math.log((a_min/a_max),2)
Alpha_a = a_max* math.exp(pow(((l+1)/(Max_iter)),2)*ln)
Beta_a =a_max* math.exp(pow(((l+1)/(Max_iter)),3)*ln)
Delta_a = (Alpha_a + Beta_a)/2
# Update the Position of search agents including omegas
for i in range(0,SearchAgents_no):
for j in range (0,dim):
r1 = random.random() # r1 is a random number in [0,1]
r2 = random.random() # r2 is a random number in [0,1]
# r1 = 0.2*Alpha_score
# r2= 0.2*Alpha_score
A1=2*Alpha_a*r1-Alpha_a # Equation (3.3)
C1=2*r2 # Equation (3.4)
D_alpha=abs(C1*Alpha_pos[j]-Positions[i,j]) # Equation (3.5)-part 1
X1=Alpha_pos[j]-A1*D_alpha # Equation (3.6)-part 1
r1=random.random()
r2=random.random()
# r1 = random.random() # r1 is a random number in [0,1]
# r2 = random.random() # r2 is a random number in [0,1]
A2=2*Beta_a*r1-Beta_a; # Equation (3.3)
C2=2*r2; # Equation (3.4)
D_beta=abs(C2*Beta_pos[j]-Positions[i,j]) # Equation (3.5)-part 2
X2=Beta_pos[j]-A2*D_beta # Equation (3.6)-part 2
#
r1=random.random()
r2=random.random()
# r1 = random.random() # r1 is a random number in [0,1]
# r2 = random.random() # r2 is a random number in [0,1]
A3=2*Delta_a*r1-Delta_a # Equation (3.3)
C3=2*r2 # Equation (3.4)
D_delta=abs(C3*Delta_pos[j]-Positions[i,j]); # Equation (3.5)-part 3
X3=Delta_pos[j]-A3*D_delta # Equation (3.5)-part 3
Positions[i,j]=(X1+X2+X3)/3 # Equation (3.7)
Convergence_curve[l]=Alpha_score;
if (l%1==0):
print(['At iteration '+ str(l)+ ' the best fitness is '+ str(Alpha_score)]);
timerEnd=time.time()
s.endTime=time.strftime("%Y-%m-%d-%H-%M-%S")
s.executionTime=timerEnd-timerStart
s.convergence=Convergence_curve
s.optimizer= str(Alpha_pos)
s.objfname="IGWO (0.2fitness)-1000data- 10 pop - 5itr "
return s
def assignValue(pos):
wolfPos = []
for i in pos:
wolfPos.append(i)
return np.array(wolfPos)
def binerization(pos):
k = 0
h = 0
for i in pos:
h = 0
for j in i:
if (j <= 0.5):
pos[k, h] = 0
else:
pos[k, h] = 1
h+=1
k+=1
return pos