【优化算法】混沌博弈优化算法（CGO）【含Matlab源码 1803期】

一、获取代码方式

获取代码方式1：
完整代码已上传我的资源：【优化算法】混沌博弈优化算法（CGO）【含Matlab源码 1803期】

二、部分源代码

%  Chaos Game Optimization (CGO) source codes version 1.0           
% 
clc;clear all;                                                              
%% Get Required Problem Information
ObjFuncName = @(x) Sphere(x); % @YourCostFunction ;
Var_Number = 10 ; % Number of your variables ;
LB = -10 *ones(1,Var_Number) ;  % Lower bound of variable ;
UB = 10 *ones(1,Var_Number) ;  % Upper bound of variable ;
%% Get Required Algorithm Parameters
MaxIter = 100 ; % Maximum number of generations ;
Seed_Number = 25 ; % Maximum number of initial eligible points ;
%% Outputs:
% BestSeed        (Best solution)
% BestFitness     (final Best fitness)
% Conv_History    (Convergence History Curve)
%% initialization
for i=1:Seed_Number   
    % Initializing the Position of initial eligible points
    Seed(i,:)=unifrnd(LB,UB);
    % Initializing the fitness of initial eligible points
    Fun_eval(i,:)=feval(ObjFuncName,Seed(i,:));
end
%% Search Process of the CGO
for Iter=1:MaxIter
    for i=1:Seed_Number
        % Update the best Seed
        [~,idbest]=min(Fun_eval);
        BestSeed=Seed(idbest,:);
        %% Generate New Solutions
        % Random Numbers
        I=randi([1,2],1,12); % Beta and Gamma
        Ir=randi([0,1],1,5);
        % Random Groups
        RandGroupNumber=randperm(Seed_Number,1);
        RandGroup=randperm(Seed_Number,RandGroupNumber);
        % Mean of Random Group
        MeanGroup=mean(Seed(RandGroup,:)).*(length(RandGroup)~=1)...
            +Seed(RandGroup(1,1),:)*(length(RandGroup)==1);   
        % New Seeds
        Alfa(1,:)=rand(1,Var_Number);
        Alfa(2,:)= 2*rand(1,Var_Number)-1;
        Alfa(3,:)= (Ir(1)*rand(1,Var_Number)+1);
        Alfa(4,:)= (Ir(2)*rand(1,Var_Number)+(~Ir(2)));   
        ii=randi([1,4],1,3);
        SelectedAlfa=Alfa(ii,:);

        NewSeed(1,:)=Seed(i,:)+SelectedAlfa(1,:).*(I(1)*BestSeed-I(2)*MeanGroup);
        NewSeed(2,:)=BestSeed+SelectedAlfa(2,:).*(I(3)*MeanGroup-I(4)*Seed(i,:));
        NewSeed(3,:)=MeanGroup+SelectedAlfa(3,:).*(I(5)*BestSeed-I(6)*Seed(i,:));
        NewSeed(4,:)=unifrnd(LB,UB);
        for j=1:4
            % Checking/Updating the boundary limits for Seeds
            NewSeed(j,:)=bound(NewSeed(j,:),UB,LB);
            % Evaluating New Solutions
            Fun_evalNew(j,:)=feval(ObjFuncName, NewSeed(j,:));
        end
        Seed=[Seed; NewSeed];       
        Fun_eval=[Fun_eval; Fun_evalNew];
    end
    [Fun_eval, SortOrder]=sort(Fun_eval);
    Seed=Seed(SortOrder,:);
    [BestFitness,idbest]=min(Fun_eval);   
    BestSeed=Seed(idbest,:);
    Seed=Seed(1:Seed_Number,:);
    Fun_eval=Fun_eval(1:Seed_Number,:);
    % Store Best Cost Ever Found
    Conv_History(Iter)=BestFitness;
    % Show Iteration Information
    disp(['Iteration ' num2str(Iter) ': Best Cost = ' num2str(Conv_History(Iter))]);
end
   



  

 

  
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三、运行结果

四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1] 包子阳,余继周,杨杉.智能优化算法及其MATLAB实例（第2版）[M].电子工业出版社，2016.
[2]张岩,吴水根.MATLAB优化算法源代码[M].清华大学出版社，2017.

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