【SVM分类】基于布谷鸟算法优化实现SVM数据分类matlab源码

clc;
clear;
close all;
%% 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
%% ABC Settings
MaxIt=200;              % Maximum Number of Iterations
nPop=100;               % Population Size (Colony Size)
nOnlooker=nPop;         % Number of Onlooker Bees
L=round(0.6*nVar*nPop); % Abandonment Limit Parameter (Trial Limit)
a=1;                    % Acceleration Coefficient Upper Bound
%% Initialization
% Empty Bee Structure
empty_bee.Position=[];
empty_bee.Cost=[];
% Initialize Population Array
pop=repmat(empty_bee,nPop,1);
% Initialize Best Solution Ever Found
BestSol.Cost=inf;
% Create Initial Population
for i=1:nPop
    pop(i).Position=unifrnd(VarMin,VarMax,VarSize);
    pop(i).Cost=CostFunction(pop(i).Position);
    if pop(i).Cost<=BestSol.Cost
        BestSol=pop(i);
    end
end
% Abandonment Counter
C=zeros(nPop,1);
% Array to Hold Best Cost Values
BestCost=zeros(MaxIt,1);
%% ABC Main Loop
for it=1:MaxIt
    % Recruited Bees
    for i=1:nPop
        % Choose k randomly, not equal to i
        K=[1:i-1 i+1:nPop];
        k=K(randi([1 numel(K)]));
        % Define Acceleration Coeff.
        phi=a*unifrnd(-1,+1,VarSize);
        % New Bee Position
        newbee.Position=pop(i).Position+phi.*(pop(i).Position-pop(k).Position);
        % Evaluation
        newbee.Cost=CostFunction(newbee.Position);
        % Comparision
        if newbee.Cost<=pop(i).Cost
            pop(i)=newbee;
        else
            C(i)=C(i)+1;
        end
    end
    % Calculate Fitness Values and Selection Probabilities
    F=zeros(nPop,1);
    MeanCost = mean([pop.Cost]);
    for i=1:nPop
        F(i) = exp(-pop(i).Cost/MeanCost); % Convert Cost to Fitness
    end
    P=F/sum(F);
    % Onlooker Bees
    for m=1:nOnlooker
        % Select Source Site
        i=RouletteWheelSelection(P);
        % Choose k randomly, not equal to i
        K=[1:i-1 i+1:nPop];
        k=K(randi([1 numel(K)]));
        % Define Acceleration Coeff.
        phi=a*unifrnd(-1,+1,VarSize);
        % New Bee Position
        newbee.Position=pop(i).Position+phi.*(pop(i).Position-pop(k).Position);
        % Evaluation
        newbee.Cost=CostFunction(newbee.Position);
        % Comparision
        if newbee.Cost<=pop(i).Cost
            pop(i)=newbee;
        else
            C(i)=C(i)+1;
        end
    end
    % Scout Bees
    for i=1:nPop
        if C(i)>=L
            pop(i).Position=unifrnd(VarMin,VarMax,VarSize);
            pop(i).Cost=CostFunction(pop(i).Position);
            C(i)=0;
        end
    end
    % Update Best Solution Ever Found
    for i=1:nPop
        if pop(i).Cost<=BestSol.Cost
            BestSol=pop(i);
        end
    end
    % Store Best Cost Ever Found
    BestCost(it)=BestSol.Cost;
    % Display Iteration Information
    disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]);
end
%% Results
figure;
%plot(BestCost,'LineWidth',2);
semilogy(BestCost,'LineWidth',2);
xlabel('Iteration');
ylabel('Best Cost');
grid on;