基于MATLAB的头脑风暴优化算法

Posted 2021-09-05 fpga&matlab

% bso test
clc
clear all
close all
%funStr = 'rastrigin'; %output worksheet name
%funName = @rastrigin; % fitness function name
%n_p = 500;    % population size
%n_d = 2;    % dimension
%n_c = 5;    % number of clusters
%rang_l = -100;    % left of dynamic range
%rang_r = 100;    % right of dynamic range
%max_iteration = 50;    % maximal number of iterations

warning off all

n_p = 100;    % population size
n_d = 30;    % dimension
n_c = 5;    % number of clusters

funStr = 'sphere30D5C'; %output worksheet name
funName = @sphere; % fitness function name
rang_l = -100;    % left of dynamic range sphere
rang_r = 100;    % right of dynamic range

%funStr = 'rastrigin10D5C'; %output worksheet name
%funName = @rastrigin; % fitness function name
%rang_l = -5.12;    % left of dynamic range rastrigin
%rang_r = 5.12;    % right of dynamic range

max_iteration = 200;    % maximal number of iterations

for idx = 1:50  % run times
    idx
    fit = bso2(funName,n_p,n_d,n_c,rang_l,rang_r,max_iteration);  %run BSO one time
%     if idx <27
%         str = native2unicode(idx + 64);
%     else % assume idx <53
%         str =['A',native2unicode(idx + 38)];
%     end
%     xlswrite('bso2.xls',fit,funStr, [str,'1']); % output best fitness over generation to EXCEL worksheet for each BSO run
%     ['run', num2str(idx)]
    fit1=fit';
    fits(idx,1:max_iteration)=fit1(1,1:max_iteration);
     
end
  fits=sum(fits)/idx;
  plot(fits,'-.');
 

 

B-56