跳跃时变加速度系数的自组织分层粒子群算法附matlab代码.zip

% % Based on the paper: % % Mojtaba Ghasemi, Ebrahim Akbari, Mohammad Zand, Morteza Hadipour, % % Sahand Ghavidel, and Li Li. "An Efficient Modified HPSO-TVAC-Based % % Dynamic Economic Dispatch of Generating Units." Electric Power % % Components and Systems 47, no. 19-20 (2019): 1826-1840. clear tic clc disp('MNHPSO-JTVAC'); CostFunction=@(x) ObjFun(x); nVar=30; % Number of decision variables VarMin=-100; % Minimum value of decision variables VarMax=-VarMin; % Maximum value of decision variables nPop=100; % Number of algorithm population npop_0=nPop; npop_f=20; ci=0.5; cf=0.0; dx=VarMax-VarMin; vmax(1:nVar)=0.5*dx; MaxNFE=100e3; % Maximum Number of Function Evaluations MaxIt=MaxNFE/npop_f; velocity=zeros(nPop,nVar); position=velocity; cost=zeros(1,nVar); gbestcost=inf; pbest=position; pbestcost=cost; gcost=nan(1,MaxIt); NFES=zeros(1,MaxIt); NFE=0; it=0; while NFE<=MaxNFE it=it+1; if NFE==0 for i=1:nPop velocity(i,1:nVar)=vmax; position(i,:)=VarMin+rand(1,nVar).*(VarMax-VarMin); cost(i)=CostFunction(position(i,:)); NFE=NFE+1; pbest(i,:)=position(i,:); pbestcost(i)=cost(i); if pbestcost(i)<gbestcost gbest=pbest(i,:); gbestcost=pbestcost(i); end end gcost(1)=gbestcost; else c_it=((cf-ci)*(NFE/MaxNFE))+ci; % % Population Reduction Policy nPop=npop_0-(npop_0-npop_f)*(NFE/MaxNFE); nPop=round(nPop); nPop=max(nPop,npop_f); nPop=min(npop_0,nPop); for i=1:nPop % % Select a random particle other than the i-th one a0=randi(nPop-1,1); if a0>=i a0=a0+1; end w=randn; c1_it=(abs(w))^(c_it*w); c2_it=(abs(1-w))^(c_it*(1/(1-w))); %%%%%%%%%%%%% velocity(i,:)=(c1_it*(rand(1,nVar)).*((pbest(i,:))-position(i,:)))... +(c2_it*(rand(1,nVar)).*((gbest(1,:)+pbest(a0,:))-2*position(i,:))); for gg=1:nVar if velocity(i,gg)==0 if rand<0.5 velocity(i,gg)=rand*vmax(1,gg); else velocity(i,gg)=-rand*vmax(1,gg); end end velocity(i,gg)=sign(velocity(i,gg)).*min(abs(velocity(i,gg)),abs(vmax(1,gg))); end %%%%%%%%%%%%%%%% velocity(i,:)=min(max(velocity(i,:),-vmax),vmax); position(i,:)=position(i,:)+velocity(i,:); position(i,:)=min(max(position(i,:),VarMin),VarMax); cost(i)=CostFunction(position(i,:)); NFE=NFE+1; if cost(i)<pbestcost(i) pbest(i,:)=position(i,:); pbestcost(i)=cost(i); if pbestcost(i)<gbestcost gbest=pbest(i,:); gbestcost=pbestcost(i); end end end end NFES(it)=NFE; gcost(it)=gbestcost; fprintf('Iteration %3.0f, NFE %6.0f, Best Cost %g\n',it,NFE,gbestcost) end gcost(it+1:end)=[]; NFES(it+1:end)=[]; plot(NFES,gcost,'b','linewidth',2); BestSol.Position=gbest; BestSol.Cost=gbestcost;