Classic-Methods.rar

clc; clear; close all; %% Problem Definition nVar=3; % Number of Decision Variables VarSize=[1 nVar]; % Decision Variables Matrix Size VarMin=-5; % Lower Bound of Variables VarMax= 5; % Upper Bound of Variables %% Weighted-Sum Approach t1=inf; for k=1:50 x0=unifrnd(VarMin,VarMax,VarSize); [~, t1k]=fminunc(@Objective1,x0); if t1k<t1 t1=t1k; end end t2=inf; for k=1:50 x0=unifrnd(VarMin,VarMax,VarSize); [~, t2k]=fminunc(@Objective2,x0); if t2k<t2 t2=t2k; end end N=1000; empty_sol.Position=[]; empty_sol.Cost=[]; sol=repmat(empty_sol,N,1); w1=linspace(0,1,N); w2=1-w1; for i=1:N FWS=@(x) w1(i)*(Objective1(x)-t1)+w2(i)*(Objective2(x)-t2); x0=unifrnd(VarMin,VarMax,VarSize); options=optimset('MaxIter',1000,'MaxFunEvals',1000); sol(i).Position=fminunc(FWS,x0); sol(i).Cost=MyCost(sol(i).Position); end sol=GetNonDominatedSolutions(sol); Costs=[sol.Cost]; figure; plot(Costs(1,:),Costs(2,:),'.');