基于带电粒子算法求解单目标优化问题附matlab代码

function [bestX,bestFitness,bestFitnessEvolution]=ECPO_v1(options) %-------------------------------------------------------------------------- bestFitnessEvolution=[]; %-------------------------------------------------------------------------- ECPSize = options.ECPSize; MaxIter = options.MaxIter; % MaxIter: maximum number of iterations Strategy=options.V(1); NPI=options.V(2); archSize=ECPSize/options.V(3); ObjFunction=options.ObjFunction; lb=options.lb; % Lower boundaries ub=options.ub; % Upper boundaries ProblemSize=options.ProblemSize; % Number of the decision variables %-------------------------------------------------------------------------- % Initialization ECP=repmat(lb,ECPSize,1)+rand(ECPSize,ProblemSize).*repmat((ub-lb),ECPSize,1); F_ECP = feval(ObjFunction,ECP); nEval=ECPSize; [F_ECP, ind] = sort(F_ECP); ECP = ECP(ind,:); if Strategy==1 pop_fac=2*nchoosek(NPI,2); elseif Strategy==2 pop_fac=NPI; elseif Strategy==3 pop_fac=2*nchoosek(NPI,2)+NPI; end % Main Loop for iter=1:MaxIter pop_arch = ECP(1:archSize,:); F_arch=F_ECP(1:archSize); newECP=[]; newECP1=[]; newECP2=[]; for i=1:1:ceil(ECPSize/pop_fac) % generate beta = Gaussian number, with mean=0.7 and standard % deviation=0.1 Force = normrnd(0.7,0.2); SP=sort(randperm(ECPSize,NPI)); SP=SP(1:NPI); if Strategy==1 for ii=1:NPI for jj=1:NPI S1=ECP(SP(ii),:)+ Force * (ECP(1,:) - ECP(SP(ii),:)); if jj<ii S1= S1 + Force * (ECP(SP(jj),:) - ECP(SP(ii),:)); newECP(end+1,:)=S1; elseif jj>ii S1= S1 - Force * (ECP(SP(jj),:) - ECP(SP(ii),:)); newECP(end+1,:)=S1; end end end elseif Strategy==2 for ii=1:NPI S1=ECP(SP(ii),:)+ 0*Force * (ECP(1,:) - ECP(SP(ii),:)); for jj=1:NPI if jj<ii S1= S1 + Force * (ECP(SP(jj),:) - ECP(SP(ii),:)); elseif jj>ii S1= S1 - Force * (ECP(SP(jj),:) - ECP(SP(ii),:)); end end newECP(end+1,:)=S1; end elseif Strategy==3 for ii=1:NPI S2=ECP(SP(ii),:)+ 1*Force * (ECP(1,:) - ECP(SP(ii),:)); for jj=1:NPI S1=ECP(SP(ii),:)+ Force * (ECP(1,:) - ECP(SP(ii),:)); if jj<ii S1= S1 + Force * (ECP(SP(jj),:) - ECP(SP(ii),:)); S2= S2 + Force * (ECP(SP(jj),:) - ECP(SP(ii),:)); newECP1(end+1,:)=S1; elseif jj>ii S1= S1 - Force * (ECP(SP(jj),:) - ECP(SP(ii),:)); S2= S2 - Force * (ECP(SP(jj),:) - ECP(SP(ii),:)); newECP1(end+1,:)=S1; end end newECP2(end+1,:)=S2; end newECP=[newECP1; newECP2]; end end newECP = bound(newECP,lb, ub); for i1=1:size(newECP,1) for j=1:ProblemSize r=rand; if (r<0.2) pos= randi(archSize(1)); newECP(i1,j)= pop_arch(pos,j); end end end ECP_All=[pop_arch; newECP]; F_All=[F_arch feval(ObjFunction,newECP)]; nEval=nEval+size(newECP,1); [F_All, index] = sort(F_All); ECP_All = ECP_All( index, : ); ECP = ECP_All (1:ECPSize,:); F_ECP = F_All(1:ECPSize); bestX=ECP(1,:); bestFitnessEvolution(iter)=F_ECP(1); if options.Display_Flag==1 fprintf('Iteration N° is %g Best Fitness is %g\n',iter,F_ECP(1)) end end bestFitness=F_ECP(1); end function [x]=bound(x,l,u) for j = 1:size(x,1) x(j,x(j,:)<l)=l(x(j,:)<l); x(j,x(j,:)>u)=u(x(j,:)>u); end end