【单目标优化求解】基于matlab混沌算法求解单目标问题【含Matlab源码 1410期】

一、获取代码方式

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二、部分源代码

clear all
close all
clc

N = 50;                              % Number of Searcher Agents "
Max_Iteration  = 100;                % Maximum number of "iterations"
Q=1;            % ACO Parameter
tau0=10;        % Initial Phromone             (ACO)
alpha=0.3;      % Phromone Exponential Weight  (ACO)
rho=0.1;        % Evaporation Rate             (ACO)
beta_min=0.2;   % Lower Bound of Scaling Factor (DE)
beta_max=0.8;   % Upper Bound of Scaling Factor (DE)
pCR=0.2;        % Crossover Probability         (DE)
 ElitistCheck=1; % GSA Parameter
 Rpower=1;       % GSA Parameter
 min_flag=1; % 1: minimization, 0: maximization (GSA)
 Algorithm_num=2;  %% These are 10 chaotic maps mentioned in the paper. So, change the value of Algorithm_num from 2 to 11 as 1 is for standard GSA.
 chValueInitial=20; % CGSA

 Benchmark_Function_ID=1 %Benchmark function ID

    RunNo  = 20; 
    
for k = [ 1 : 1 : RunNo ]   
% % %   
[gBestScore,gBest,GlobalBestCost]= CPSOGSA(Benchmark_Function_ID, N, Max_Iteration);
BestSolutions1(k) = gBestScore;
% [Fbest,Lbest,BestChart]=GSA(Benchmark_Function_ID,N,Max_Iteration,ElitistCheck,min_flag,Rpower);
% BestSolutions2(k) = Fbest;
%   [PcgCurve,GBEST]=pso(Benchmark_Function_ID,N,Max_Iteration);
%      BestSolutions3(k) = GBEST.O;
%  [BestCost,BestSol] = bbo( Benchmark_Function_ID, N, Max_Iteration);
%      BestSolutions4(k) = BestSol.Cost;
% [BestSolDE,DBestSol,BestCostDE] = DE(Benchmark_Function_ID, N, Max_Iteration,beta_min,beta_max,pCR);
% BestSolutions5(k) = BestSolDE.Cost ;
%   [BestSolACO,BestAnt,BestCostACO] = ACO(Benchmark_Function_ID, N, Max_Iteration,Q,tau0,alpha,rho);
% BestSolutions6(k) = BestSolACO.Cost ;
% [Best_score,Best_pos,SSA_cg_curve]=SSA(Benchmark_Function_ID, N, Max_Iteration);
% BestSolutions7(k) = Best_score ;
% [Best_scoreSCA,Best_pos,SCA_cg_curve]=SCA(Benchmark_Function_ID, N, Max_Iteration);
% BestSolutions8(k) = Best_scoreSCA ;
% [Best_scoreGWO,Best_pos,GWO_cg_curve]=GWO(Benchmark_Function_ID, N, Max_Iteration);
% BestSolutions9(k) = Best_scoreGWO ;

%  if Algorithm_num==6
%     [CFbest,CLbest1,CBestChart]=CGSA(Benchmark_Function_ID,N,Max_Iteration,ElitistCheck,min_flag,Rpower,Algorithm_num,chValueInitial);
%     BestSolutions10(k) = CFbest ;
%  end
 
    Average= mean(BestSolutions1);
    StandDP=std(BestSolutions1);
    Med = median(BestSolutions1); 
    [BestValueP I] = min(BestSolutions1);
    [WorstValueP IM]=max(BestSolutions1);

     disp(['Run # ' , num2str(k), ' gBestScore:  ' , num2str( gBestScore)]);
%      disp(['Run # ' , num2str(k), ' Fbest:  ' , num2str( Fbest)]);
%      disp(['Run # ' , num2str(k), ' GBEST.O: ' , num2str( GBEST.O)]);
%      disp(['Run # ' , num2str(k), ' BestSol.Cost:  ' , num2str( BestSol.Cost)]);
%      disp(['Run # ' , num2str(k), ' BestSolDE.Cost :  ' , num2str( BestSolDE.Cost)]);
%      disp(['Run # ' , num2str(k), ' BestSolACO.Cost:  ' , num2str( BestSolACO.Cost )]);
%      disp(['Run # ' , num2str(k), ' Best_score :  ' , num2str( Best_score)]);
%      disp(['Run # ' , num2str(k), ' Best_scoreSCA :  ' , num2str( Best_scoreSCA)]);
%      disp(['Run # ' , num2str(k), ' Best_scoreGWO :  ' , num2str( Best_scoreGWO)]);      
%      disp(['Run # ' , num2str(k), ' CFbest :  ' , num2str( CFbest ),' Algorithm_num= ' , num2str(Algorithm_num)])
end




     disp([ 'Best=',num2str( BestValueP)]);
     disp(['Worst=',num2str(WorstValueP)]);
     disp(['Average=',num2str( Average)]);
     disp([ 'Standard Deviation=',num2str( StandDP)]);
     disp(['Median=',num2str(Med)]);



    figure
    
   semilogy(1:Max_Iteration,GlobalBestCost,'DisplayName','CPSOGSA', 'Color', 'r','Marker','diamond','LineStyle','-','LineWidth',2,...
       'MarkerEdgeColor','r','MarkerFaceColor',[.49 1 .63],'MarkerSize',2);
%    hold on
%    semilogy(1:Max_Iteration,BestChart,'DisplayName','GSA','Color','g','Marker','o','LineStyle','-','LineWidth',2,...
%         'MarkerEdgeColor','g','MarkerFaceColor',[.49 1 .63],'MarkerSize',2);
%    semilogy(1:Max_Iteration,PcgCurve,'DisplayName','PSO','Color','c','Marker','square','LineStyle','-','LineWidth',2,...
%        'MarkerEdgeColor','c','MarkerFaceColor',[.49 1 .63],'MarkerSize',2);
%    semilogy(1:Max_Iteration,BestCost,'DisplayName','BBO','Color','b','Marker','*','LineStyle','-','LineWidth',2,...
%        'MarkerEdgeColor','b','MarkerFaceColor',[.49 1 .63],'MarkerSize',2);
%    semilogy(1:Max_Iteration,BestCostDE,'DisplayName','DE','Color','y','Marker','+','LineStyle','-','LineWidth',2,...
%        'MarkerEdgeColor','y','MarkerFaceColor',[.49 1 .63],'MarkerSize',2);
%    semilogy(1:Max_Iteration,BestCostACO,'DisplayName','ACO','Color','m','LineStyle','-','LineWidth',2);
%    semilogy(1:Max_Iteration,SSA_cg_curve,'DisplayName','SSA','Color','c','LineStyle','-','LineWidth',2);
%    semilogy(1:Max_Iteration,SCA_cg_curve,'DisplayName','SCA','Color','b','LineStyle','--','LineWidth',2);
%    semilogy(1:Max_Iteration,GWO_cg_curve,'DisplayName','GWO','Color','r','LineStyle','--','LineWidth',2);
%    semilogy(1:Max_Iteration,CBestChart,'DisplayName','CGSA','Color','m','LineStyle',':','LineWidth',2);
    
    

title ('\fontsize{15}\bf Welded Beam Design');
% title ('\fontsize{15}\bf Compression Spring Design');
% title ('\fontsize{15}\bf Pressure Vessel Design');

 xlabel('\fontsize{15}\bf Iteration');
 ylabel('\fontsize{15}\bf Fitness(Best-so-far)');
 legend('\fontsize{12}\bf CPSOGSA');
 
%  legend('\fontsize{12}\bf CPSOGSA','\fontsize{12}\bf GSA','\fontsize{12}\bf PSO','\fontsize{12}\bf BBO',...
%     '\fontsize{12}\bf DE','\fontsize{12}\bf ACO','\fontsize{12}\bf GWO','\fontsize{12}\bf SCA','\fontsize{12}\bf SSA','\fontsize{12}\bf CGSA',1);

    


  

 

  
 
 

  
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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
《智能优化算法及其MATLAB实例（第2版）》包子阳 余继周 杨杉著 电子工业出版社

文章来源: qq912100926.blog.csdn.net，作者：海神之光，版权归原作者所有，如需转载，请联系作者。

原文链接：qq912100926.blog.csdn.net/article/details/120858477