standard and probabilistic bee algorithm explanation

Bisrat Yeshidagna

2020 6 12

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%% Problem Definition
CostFunction=@(x) Sphere(x);        % Cost Function
nVar=5;             % Number of Decision Variables
VarSize=[1 nVar];   % Decision Variables Matrix Size
VarMin=-10;         % Decision Variables Lower Bound
VarMax= 10;         % Decision Variables Upper Bound
 
%% Bees Algorithm Parameters
MaxIt=1000;          % Maximum Number of Iterations
nScoutBee=30;                           % Number of Scout Bees
nSelectedSite=round(0.5*nScoutBee);     % Number of Selected Sites
nEliteSite=round(0.4*nSelectedSite);    % Number of Selected Elite Sites
nSelectedSiteBee=round(0.5*nScoutBee);  % Number of Recruited Bees for Selected Sites
nEliteSiteBee=2*nSelectedSiteBee;       % Number of Recruited Bees for Elite Sites
r=0.1*(VarMax-VarMin);	% Neighborhood Radius
rdamp=0.95;             % Neighborhood Radius Damp Rate
%% Initialization
% Empty Bee Structure
empty_bee.Position=[];
empty_bee.Cost=[];
% Initialize Bees Array
bee=repmat(empty_bee,nScoutBee,1);
% Create New Solutions
for i=1:nScoutBee
    bee(i).Position=unifrnd(VarMin,VarMax,VarSize);
    bee(i).Cost=CostFunction(bee(i).Position);
end
% Sort
[~, SortOrder]=sort([bee.Cost]);
bee=bee(SortOrder);
% Update Best Solution Ever Found
BestSol=bee(1);
% Array to Hold Best Cost Values
BestCost=zeros(MaxIt,1);
%% Bees Algorithm Main Loop
for it=1:MaxIt
    
    % Elite Sites
    for i=1:nEliteSite
        
        bestnewbee.Cost=inf;
        
        for j=1:nEliteSiteBee
            newbee.Position=PerformBeeDance(bee(i).Position,r);
            newbee.Cost=CostFunction(newbee.Position);
            if newbee.Cost<bestnewbee.Cost
                bestnewbee=newbee;
            end
        end
        if bestnewbee.Cost<bee(i).Cost
            bee(i)=bestnewbee;
        end
        
    end
    
    % Selected Non-Elite Sites
    for i=nEliteSite+1:nSelectedSite
        
        bestnewbee.Cost=inf;
        
        for j=1:nSelectedSiteBee
            newbee.Position=PerformBeeDance(bee(i).Position,r);
            newbee.Cost=CostFunction(newbee.Position);
            if newbee.Cost<bestnewbee.Cost
                bestnewbee=newbee;
            end
        end
        if bestnewbee.Cost<bee(i).Cost
            bee(i)=bestnewbee;
        end
        
    end
    
    % Non-Selected Sites
    for i=nSelectedSite+1:nScoutBee
        bee(i).Position=unifrnd(VarMin,VarMax,VarSize);
        bee(i).Cost=CostFunction(bee(i).Position);
    end
    
    % Sort
    [~, SortOrder]=sort([bee.Cost]);
    bee=bee(SortOrder);
    
    % Update Best Solution Ever Found
    BestSol=bee(1);
    
    % Store Best Cost Ever Found
    BestCost(it)=BestSol.Cost;
    
    % Display Iteration Information
    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
    
    % Damp Neighborhood Radius
    r=r*rdamp;
    
end
%% Results
figure;
%plot(BestCost,'LineWidth',2);
semilogy(BestCost,'LineWidth',2);
xlabel('Iteration');
ylabel('Best Cost');

6 个评论
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Bisrat Yeshidagna 2020-6-13

only about which is the objective function and the constraints in the bee algorithm

Bisrat Yeshidagna 2020-6-13

I have some knowladge about matlab and optimization.i want to modify it for my research that is why i want about the abjective function and constriants.

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