ABC_MATLAB_web.rar

				%%%%%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',  10, ...   % 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',       10, ... % Number of parameters of the objective function   
				    'Limit',   100, ... % Control paramter in order to abandone the food source 
				    'lb',  -10, ... % Lower bound of the parameters to be optimized
				    'ub',  10, ... %Upper bound of the parameters to be optimized
				    'ObjFun' , 'rosenbrock', ... %Write the name of the objective function you want to minimize
				    'RunTime',3); % 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 				    
				    %%%%% 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)				             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				    if(rand				        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)				             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=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
				
				semilogy(mean(GlobalMins));
				title('Mean of Best function values');
				xlabel('cycles');
				ylabel('error');
				fprintf('Mean =%g Std=%g\n',mean(GlobalMins(:,end)),std(GlobalMins(:,end)));