人工蜂群算法

%%%%%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',  20, ...   % Number of Employed Bees+ Number of Onlooker Bees
    'MaxCycles', 2000,...   % 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',       30 , ... % Number of parameters of the objective function
    'Limit',   100, ... % Control paramter in order to abandone the food source
    'lb',  -30, ... % Lower bound of the parameters to be optimized
    'ub',  30, ... %Upper bound of the parameters to be optimized
    'ObjFun' , 'Rosenbrock', ... %Write the name of the objective function you want to minimize
    'RunTime',10); % 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;

        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;
                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;

            i=i+1;
            if (i==(ABCOpts.ColonySize/2)+1)
                i=1;
            end;
        end;

        %%%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(mean(GlobalMins))
    title('Mean of Best function values');
    xlabel('cycles');
    ylabel('error');
    fprintf('Mean =%g Std=%g\n',mean(GlobalMins(:,end)),std(GlobalMins(:,end)));
end

%--------------------------------------------------------------------------
% 解输出

GlobalMin
GlobalParams

标签：蜂群,ObjEmp,end,Employed2,人工,Param2Change,算法,Employed,ABCOpts
来源： https://blog.csdn.net/ccsss22/article/details/113852288