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GMFEA.m
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GMFEA.m
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function dataGMFEA = GMFEA(tasks,pop,nGen,selectionProcess,rmp,pIL,nRepeat,idxTask,dq,initPop)
% G-MFEA function: implementation of G-MFEA algorithm
tic
dataDisp=cell(1,3);
dataDisp{1}=idxTask; dataDisp{2}='GMFEA';
nTasks=length(tasks);
if nTasks <= 1
error('At least 2 tasks required for GMFEA');
end
while mod(pop,nTasks) ~= 0
pop = pop + 1;
end
D=zeros(1,nTasks);
for i=1:nTasks
D(i)=tasks(i).dims;
end
D_multitask=max(D);
options = optimoptions(@fminunc,'Display','off','Algorithm','quasi-newton','MaxIter',2); % settings for individual learning
fncevalCalls = zeros(1,nRepeat);
callsPerIndividual=zeros(1,pop);
evBestFitness = zeros(nTasks*nRepeat,nGen); % best fitness found
TotalEvaluations=zeros(nRepeat,nGen); % total number of task evaluations so fer
bestobj=Inf(1,nTasks);
for rep = 1:nRepeat
dataDisp{3}=rep;
dq.send(dataDisp);
for i = 1 : pop
population(i) = Chromosome();
population(i) = initialize(population(i),D_multitask);
population(i).skill_factor=0;
end
for n=1:nTasks
if nargin>=10
for i=1:pop/nTasks
population((n-1)*pop/nTasks+i).rnvec(1:D(n))=initPop{n,rep}(i,1:D(n));
end
else
initPop{n,rep}=reshape([population((n-1)*pop/nTasks+(1:pop/nTasks)).rnvec],D(n),pop/nTasks)';
end
end
for i = 1 : pop
[population(i),callsPerIndividual(i)] = evaluate(population(i),tasks,pIL,nTasks,options);
end
fncevalCalls(rep)=fncevalCalls(rep) + sum(callsPerIndividual);
TotalEvaluations(rep,1)=fncevalCalls(rep);
factorial_cost=zeros(1,pop);
for i = 1:nTasks
for j = 1:pop
factorial_cost(j)=population(j).factorial_costs(i);
end
[xxx,y]=sort(factorial_cost);
population=population(y);
for j=1:pop
population(j).factorial_ranks(i)=j;
end
bestobj(i)=population(1).factorial_costs(i);
evBestFitness(i+2*(rep-1),1)=bestobj(i);
bestIndData(rep,i)=population(1);
end
for i=1:pop
[xxx,yyy]=min(population(i).factorial_ranks);
x=find(population(i).factorial_ranks == xxx);
equivalent_skills=length(x);
if equivalent_skills>1
population(i).skill_factor=x(randi(equivalent_skills,1));
tmp=population(i).factorial_costs(population(i).skill_factor);
population(i).factorial_costs(1:nTasks)=inf;
population(i).factorial_costs(population(i).skill_factor)=tmp;
else
population(i).skill_factor=yyy;
tmp=population(i).factorial_costs(population(i).skill_factor);
population(i).factorial_costs(1:nTasks)=inf;
population(i).factorial_costs(population(i).skill_factor)=tmp;
end
end
mu = 2; % Index of Simulated Binary Crossover (tunable)
mum = 5; % Index of polynomial mutation
threshold_trans=round(.1*nGen); % The threshold of triggering the variable translation strategy
frequency_trans=round(.02*nGen); % The frequency of changing the translate direction
sf=1.25; % The scale factor
perBest=0.4; % The percentage of the best solutions to estimate current optimums
cp=0.5*ones(1,D_multitask); % The designated location to which all optimums are transferred to
generation=1;
d=zeros(nTasks,D_multitask);
while generation < nGen
generation = generation + 1;
tempopulation=population;
if generation>threshold_trans
% Update the translated directions of each task
if mod(generation,frequency_trans)==0
for i=1:nTasks
x=find([population.skill_factor] == i);
numBest=round(perBest*length(x));
m=mean(reshape([population(x(1:numBest)).rnvec],D_multitask,numBest),2)';
d(i,:)=sf*((generation/nGen)^2)*(cp-m);
end
end
% Update the population by the translated directions of each task
for i=1:pop
tempopulation(i).rnvec=population(i).rnvec+d(population(i).skill_factor,:);
end
end
indorder = randperm(pop);
count=1;
for i = 1 : pop/2
p1 = indorder(i);
p2 = indorder(i+(pop/2));
% The decision variable shuffling strategy
D_max=max(D(tempopulation(p1).skill_factor),D(tempopulation(p2).skill_factor));
Dorder=zeros(nTasks,D_max);
if D(tempopulation(p1).skill_factor)<D_max
% randomly select one individual from P that has the same
% skill factor as p2
x=find([tempopulation.skill_factor] == tempopulation(p2).skill_factor);
same_skills=length(x);
temp=randi(same_skills);
temp_ind=tempopulation(x(temp)).rnvec;
% Randomly perturb the order of L1
Dorder(tempopulation(p1).skill_factor,:) = randperm(D_max);
temp_ind(Dorder(tempopulation(p1).skill_factor,1:D(tempopulation(p1).skill_factor)))=tempopulation(p1).rnvec(1:D(tempopulation(p1).skill_factor));
tempopulation(p1).rnvec=temp_ind;
elseif D(tempopulation(p2).skill_factor)<D_max
x=find([tempopulation.skill_factor] == tempopulation(p1).skill_factor);
same_skills=length(x);
temp=randi(same_skills);
temp_ind=tempopulation(x(temp)).rnvec;
Dorder(tempopulation(p2).skill_factor,:) = randperm(D_max);
temp_ind(Dorder(tempopulation(p2).skill_factor,1:D(tempopulation(p2).skill_factor)))=tempopulation(p2).rnvec(1:D(tempopulation(p2).skill_factor));
tempopulation(p2).rnvec=temp_ind;
end
child(count)=Chromosome();
child(count+1)=Chromosome();
if (tempopulation(p1).skill_factor == tempopulation(p2).skill_factor) || (rand(1)<rmp) % crossover
u = rand(1,D_multitask);
cf = zeros(1,D_multitask);
cf(u<=0.5)=(2*u(u<=0.5)).^(1/(mu+1));
cf(u>0.5)=(2*(1-u(u>0.5))).^(-1/(mu+1));
child(count) = crossover(child(count),tempopulation(p1),tempopulation(p2),cf);
child(count+1) = crossover(child(count+1),tempopulation(p2),tempopulation(p1),cf);
if rand(1) < 1
child(count)=mutate(child(count),child(count),D_multitask,mum);
child(count).rnvec=child(count).rnvec-d(tempopulation(p1).skill_factor,:); % Re-transfer the offspring
child(count+1)=mutate(child(count+1),child(count+1),D_multitask,mum);
child(count+1).rnvec=child(count+1).rnvec-d(tempopulation(p2).skill_factor,:); % Re-transfer the offspring
end
sf1=1+round(rand(1));
sf2=1+round(rand(1));
if sf1 == 1 % skill factor selection
child(count).skill_factor=tempopulation(p1).skill_factor;
else
child(count).skill_factor=tempopulation(p2).skill_factor;
end
if sf2 == 1
child(count+1).skill_factor=tempopulation(p1).skill_factor;
else
child(count+1).skill_factor=tempopulation(p2).skill_factor;
end
else
child(count)=mutate(child(count),tempopulation(p1),D_multitask,mum);
child(count).rnvec=child(count).rnvec-d(tempopulation(p1).skill_factor,:);
child(count).skill_factor=tempopulation(p1).skill_factor;
child(count+1)=mutate(child(count+1),tempopulation(p2),D_multitask,mum);
child(count+1).rnvec=child(count+1).rnvec-d(tempopulation(p2).skill_factor,:);
child(count+1).skill_factor=tempopulation(p2).skill_factor;
end
% Re-change the order of decision variables
if D(child(count).skill_factor)<D_max
child(count).rnvec(1:D(child(count).skill_factor))=child(count).rnvec(Dorder(child(count).skill_factor,1:D(child(count).skill_factor)));
child(count).rnvec(D(child(count).skill_factor)+1:D_max)=tempopulation(p1).rnvec(D(child(count).skill_factor)+1:D_max);
end
if D(child(count+1).skill_factor)<D_max
child(count+1).rnvec(1:D(child(count+1).skill_factor))=child(count+1).rnvec(Dorder(child(count+1).skill_factor,1:D(child(count+1).skill_factor)));
child(count+1).rnvec(D(child(count+1).skill_factor)+1:D_max)=tempopulation(p2).rnvec(D(child(count+1).skill_factor)+1:D_max);
end
child(count).rnvec(child(count).rnvec>1)=1;
child(count+1).rnvec(child(count+1).rnvec>1)=1;
child(count).rnvec(child(count).rnvec<0)=0;
child(count+1).rnvec(child(count+1).rnvec<0)=0;
count=count+2;
end
for i = 1 : pop
[child(i),callsPerIndividual(i)] = evaluate(child(i),tasks,pIL,nTasks,options);
end
fncevalCalls(rep)=fncevalCalls(rep) + sum(callsPerIndividual);
TotalEvaluations(rep,generation)=fncevalCalls(rep);
intpopulation(1:pop)=population;
intpopulation(pop+1:2*pop)=child;
factorial_cost=zeros(1,2*pop);
for i = 1:nTasks
for j = 1:2*pop
factorial_cost(j)=intpopulation(j).factorial_costs(i);
end
[xxx,y]=sort(factorial_cost);
intpopulation=intpopulation(y);
for j=1:2*pop
intpopulation(j).factorial_ranks(i)=j;
end
if intpopulation(1).factorial_costs(i)<=bestobj(i)
bestobj(i)=intpopulation(1).factorial_costs(i);
bestIndData(rep,i)=intpopulation(1);
end
evBestFitness(i+2*(rep-1),generation)=bestobj(i);
end
for i=1:2*pop
[xxx,yyy]=min(intpopulation(i).factorial_ranks);
intpopulation(i).skill_factor=yyy;
intpopulation(i).scalar_fitness=1/xxx;
end
if strcmp(selectionProcess,'elitist')
[xxx,y]=sort(-[intpopulation.scalar_fitness]);
intpopulation=intpopulation(y);
population=intpopulation(1:pop);
elseif strcmp(selectionProcess,'roulette wheel')
for i=1:nTasks
skillGroup(i).individuals=intpopulation([intpopulation.skill_factor]==i);
end
count=0;
while count<pop
count=count+1;
skill=mod(count,nTasks)+1;
population(count)=skillGroup(skill).individuals(RouletteWheelSelection([skillGroup(skill).individuals.scalar_fitness]));
end
end
% disp(['MFEA Generation = ', num2str(generation), ' best factorial costs = ', num2str(bestobj)]);
end
end
dataGMFEA.wallClockTime=toc;
dataGMFEA.bestFitness=evBestFitness;
dataGMFEA.bestIndData=bestIndData;
dataGMFEA.totalEvals=TotalEvaluations;
dataGMFEA.initPop=initPop;
end