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MFO.py
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MFO.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Dec 27 12:46:20 2019
@author: Ibrahim Aljarah, and Ruba Abu Khurma
"""
import random
import numpy
import math
from solution import solution
import time
import transfer_functions_benchmark
import fitnessFUNs
def MFO(objf,lb,ub,dim,N,Max_iteration,trainInput,trainOutput):
#Initialize the positions of moths
# Moth_pos=numpy.random.uniform(0,1,(N,dim)) *(ub-lb)+lb #generating continuous individuals
Moth_pos=numpy.random.randint(2, size=(N,dim)) #generating binary individuals
Moth_fitness=numpy.full(N,float("inf"))
#Moth_fitness=numpy.fell(float("inf"))
Convergence_curve1=numpy.zeros(Max_iteration)
Convergence_curve2=numpy.zeros(Max_iteration)
sorted_population=numpy.copy(Moth_pos)
fitness_sorted=numpy.zeros(N)
#####################
best_flames=numpy.copy(Moth_pos)
best_flame_fitness=numpy.zeros(N)
####################
double_population=numpy.zeros((2*N,dim))
double_fitness=numpy.zeros(2*N)
double_sorted_population=numpy.zeros((2*N,dim))
double_fitness_sorted=numpy.zeros(2*N)
#########################
previous_population=numpy.zeros((N,dim));
previous_fitness=numpy.zeros(N)
s=solution()
print("MFO is optimizing \""+objf.__name__+"\"")
timerStart=time.time()
s.startTime=time.strftime("%Y-%m-%d-%H-%M-%S")
Iteration=1;
# Main loop
while (Iteration<Max_iteration+1):
# Number of flames Eq. (3.14) in the paper
Flame_no=round(N-Iteration*((N-1)/Max_iteration));
for i in range(0,N):
# Check if moths go out of the search spaceand bring it back
# Moth_pos[i,:]=numpy.clip(Moth_pos[i,:], lb, ub)
# the following statement insures that at least one feature is selected
#(i.e the randomly generated individual has at least one value 1)
while numpy.sum(Moth_pos[i,:])==0:
Moth_pos[i,:]=numpy.random.randint(2, size=(1,dim))
# evaluate moths
Moth_fitness[i]=objf(Moth_pos[i,:],trainInput,trainOutput,dim)
if Iteration==1:
# Sort the first population of moths
fitness_sorted=numpy.sort(Moth_fitness)
I=numpy.argsort(Moth_fitness)
sorted_population=Moth_pos[I,:]
#Update the flames
best_flames=sorted_population;
best_flame_fitness=fitness_sorted;
else:
#
# # Sort the moths
double_population=numpy.concatenate((previous_population,best_flames),axis=0)
double_fitness=numpy.concatenate((previous_fitness, best_flame_fitness),axis=0);
#
double_fitness_sorted =numpy.sort(double_fitness);
I2 =numpy.argsort(double_fitness);
#
#
for newindex in range(0,2*N):
double_sorted_population[newindex,:]=numpy.array(double_population[I2[newindex],:])
fitness_sorted=double_fitness_sorted[0:N]
sorted_population=double_sorted_population[0:N,:]
#
# # Update the flames
best_flames=sorted_population;
best_flame_fitness=fitness_sorted;
#
# # Update the position best flame obtained so far
Best_flame_score=fitness_sorted[0]
Best_flame_pos=sorted_population[0,:]
#
previous_population=Moth_pos;
previous_fitness=Moth_fitness;
featurecount=0
for f in range(0,dim):
if Best_flame_pos[f]==1:
featurecount=featurecount+1
# print(Best_flame_pos)
# print(Best_flame_score)
#
# Convergence_curve[Iteration-1]=(Best_flame_score)
Convergence_curve1[Iteration-1]=Best_flame_score# store the best number of features
Convergence_curve2[Iteration-1]=featurecount#store the best fitness on testing returened from F11
#Display best fitness along the iteration
if (Iteration%1==0):
print(['At iteration'+ str(Iteration+1)+' the best fitness on trainig is:'+ str(Best_flame_score)+', the best number of features: '+str(featurecount)]);
# a linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)
a=-1+Iteration*((-1)/Max_iteration);
# Loop counter
for i in range(0,N):
#
for j in range(0,dim):
if (i<=Flame_no): #Update the position of the moth with respect to its corresponsing flame
#
# D in Eq. (3.13)
distance_to_flame=abs(sorted_population[i,j]-Moth_pos[i,j])
b=1
t=(a-1)*random.random()+1;
#
# % Eq. (3.12)
Moth_pos[i,j]=distance_to_flame*math.exp(b*t)*math.cos(t*2*math.pi)+sorted_population[i,j]#update statement
ss= transfer_functions_benchmark.s1(Moth_pos[i,j])
if (random.random()<ss):
Moth_pos[i,j]=1;
else:
Moth_pos[i,j]=0;
# end
#
if i>Flame_no: # Upaate the position of the moth with respct to one flame
#
# % Eq. (3.13)
distance_to_flame=abs(sorted_population[i,j]-Moth_pos[i,j]);
b=1;
t=(a-1)*random.random()+1;
#
# % Eq. (3.12)
Moth_pos[i,j]=distance_to_flame*math.exp(b*t)*math.cos(t*2*math.pi)+sorted_population[Flame_no,j]#update statement
ss= transfer_functions_benchmark.s1(Moth_pos[i,j])
if (random.random()<ss):
Moth_pos[i,j]=1;
else:
Moth_pos[i,j]=0;
#Display best fitness along the iteration
# if (Iteration%1==0):
# print(['At iteration '+ str(Iteration)+ ' the best fitness is '+ str(Best_flame_score)]);
# Convergence_curve[Iteration-1]=(Best_flame_score)
Iteration=Iteration+1;
timerEnd=time.time()
s.endTime=time.strftime("%Y-%m-%d-%H-%M-%S")
s.executionTime=timerEnd-timerStart
s.bestIndividual=Best_flame_pos
s.convergence1=Convergence_curve1
s.convergence2=Convergence_curve2
s.optimizer="MFO"
s.objfname=objf.__name__
return s