ABC_MATLAB 第一版.rar_I67_Intelligent Systems_artificial bee_人工蜂群 m

%%%%%ARTIFICIAL BEE COLONY ALGORITHM%%%% %Artificial Bee Colony Algorithm was developed by Dervis Karaboga in 2005 %by simulating the foraging behaviour of bees. %Copyright ? 2008 Erciyes University, Intelligent Systems Research Group, The Dept. of Computer Engineering %Contact: %Dervis Karaboga (karaboga@erciyes.edu.tr ) %Bahriye Basturk Akay (bahriye@erciyes.edu.tr) clear all close all clc % Set ABC Control Parameters ABCOpts = struct( 'ColonySize', 100, ... % Number of Employed Bees+ Number of Onlooker Bees 'MaxCycles', 1000,... % Maximum cycle number in order to terminate the algorithm 'ErrGoal', 1e-20, ... % Error goal in order to terminate the algorithm (not used in the code in current version) 'Dim', 5, ... % Number of parameters of the objective function 'Limit', 50, ... % Control paramter in order to abandone the food source 'lb', -600, ... % Lower bound of the parameters to be optimized 'ub', 600, ... %Upper bound of the parameters to be optimized 'ObjFun' , 'griewank', ... %Write the name of the objective function you want to minimize 'RunTime',1); % Number of the runs GlobalMins=zeros(ABCOpts.RunTime,ABCOpts.MaxCycles); for r=1:ABCOpts.RunTime % Initialise population Range = repmat((ABCOpts.ub-ABCOpts.lb),[ABCOpts.ColonySize ABCOpts.Dim]); Lower = repmat(ABCOpts.lb, [ABCOpts.ColonySize ABCOpts.Dim]); Colony = rand(ABCOpts.ColonySize,ABCOpts.Dim) .* Range + Lower; Employed=Colony(1:(ABCOpts.ColonySize/2),:); %evaluate and calculate fitness ObjEmp=feval(ABCOpts.ObjFun,Employed); FitEmp=calculateFitness(ObjEmp); %set initial values of Bas Bas=zeros(1,(ABCOpts.ColonySize/2)); GlobalMin=ObjEmp(find(ObjEmp==min(ObjEmp),end)); GlobalParams=Employed(find(ObjEmp==min(ObjEmp),end),:); Cycle=1; while ((Cycle <= ABCOpts.MaxCycles)), %%%%% Employed phase Employed2=Employed; for i=1:ABCOpts.ColonySize/2 Param2Change=fix(rand*ABCOpts.Dim)+1; neighbour=fix(rand*(ABCOpts.ColonySize/2))+1; while(neighbour==i) neighbour=fix(rand*(ABCOpts.ColonySize/2))+1; end; Employed2(i,Param2Change)=Employed(i,Param2Change)+(Employed(i,Param2Change)-Employed(neighbour,Param2Change))*(rand-0.5)*2; if (Employed2(i,Param2Change)<ABCOpts.lb) Employed2(i,Param2Change)=ABCOpts.lb; end; if (Employed2(i,Param2Change)>ABCOpts.ub) Employed2(i,Param2Change)=ABCOpts.ub; end; end; %for end ObjEmp2=feval(ABCOpts.ObjFun,Employed2); FitEmp2=calculateFitness(ObjEmp2); [Employed ObjEmp FitEmp Bas]=GreedySelection(Employed,Employed2,ObjEmp,ObjEmp2,FitEmp,FitEmp2,Bas,ABCOpts); %Normalize NormFit=FitEmp/sum(FitEmp); %%% Onlooker phase Employed2=Employed; i=1; t=0; while(t<ABCOpts.ColonySize/2) if(rand<NormFit(i)) t=t+1; Param2Change=fix(rand*ABCOpts.Dim)+1; neighbour=fix(rand*(ABCOpts.ColonySize/2))+1; while(neighbour==i) neighbour=fix(rand*(ABCOpts.ColonySize/2))+1; end; Employed2(i,:)=Employed(i,:); Employed2(i,Param2Change)=Employed(i,Param2Change)+(Employed(i,Param2Change)-Employed(neighbour,Param2Change))*(rand-0.5)*2;%find a new value in the neighborhood if (Employed2(i,Param2Change)<ABCOpts.lb) Employed2(i,Param2Change)=ABCOpts.lb; end; if (Employed2(i,Param2Change)>ABCOpts.ub) Employed2(i,Param2Change)=ABCOpts.ub; end; ObjEmp2=feval(ABCOpts.ObjFun,Employed2); FitEmp2=calculateFitness(ObjEmp2); [Employed ObjEmp FitEmp Bas]=GreedySelection(Employed,Employed2,ObjEmp,ObjEmp2,FitEmp,FitEmp2,Bas,ABCOpts,i); end; % end if i=i+1; if (i==(ABCOpts.ColonySize/2)+1) i=1; end; end; %end while %%%Memorize Best CycleBestIndex=find(FitEmp==max(FitEmp)); CycleBestIndex=CycleBestIndex(end); CycleBestParams=Employed(CycleBestIndex,:); CycleMin=ObjEmp(CycleBestIndex); if CycleMin<GlobalMin GlobalMin=CycleMin; GlobalParams=CycleBestParams; end GlobalMins(r,Cycle)=GlobalMin; %% Scout phase ind=find(Bas==max(Bas)); ind=ind(end); if (Bas(ind)>ABCOpts.Limit) Bas(ind)=0; Employed(ind,:)=(ABCOpts.ub-ABCOpts.lb)*(0.5-rand(1,ABCOpts.Dim));%*2+ABCOpts.lb; %message=strcat('burada',num2str(ind)) end; ObjEmp=feval(ABCOpts.ObjFun,Employed); FitEmp=calculateFitness(ObjEmp); % fprintf('Cycle=%d ObjVal=%g\n',Cycle,GlobalMin); Cycle=Cycle+1; end % End of ABC end; %end of runs if ABCOpts.RunTime==1 semilogy(GlobalMins); else semilogy(mean(GlobalMins),'b'); end title('Mean of Best function values'); xlabel('cycles'); ylabel('error'); fprintf('Mean =%g Std=%g\n',mean(GlobalMins(:,end)),std(GlobalMins(:,end)));