1 简介
受人类创造性解决问题过程--头脑风暴会议的启发， 2011年史玉回老师 在第二次群体智能国际会议(The Second International Conference on Swarm Intelligence(ICSI11))中提出一种新的群智能优化算法--头脑风暴优化算法，算法采用聚 类思想搜索局部最优，通过局部最优的比较得到全局最优;采用变异思想增加了算法的多 样性，避免算法陷入局部最优，在这聚与散相辅相承的过程中搜索最优解，思想新颖，适合于解决多峰高维函数问题。
2 部分代码
clc
clear
%bso test
tic
funStr = 'sphere10D5C'; %output worksheet name
funName = @sphere; % fitness function name
rang_l = -100; % left of dynamic range sphere
rang_r = 100; % right of dynamic range
%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 = 300; % dimension
% n_c = 2; % number of clusters
% funStr = 'sphere10D5C'; %output worksheet name
% funName = @sphere; % fitness function name
% rang_l = -100; % left of dynamic range sphere
% rang_r = 100; % right of dynamic range
% funStr = 'Minima10D5C'; %output worksheet name
% funName = @Minima; % fitness function name
% rang_l = -5; % left of dynamic range rastrigin
% rang_r = 5; % right of dynamic range
% funStr = 'Griewangk10D5C'; %output worksheet name
% funName = @Griewangk; % fitness function name
% rang_l = -50; % left of dynamic range rastrigin
% rang_r = 50; % right of dynamic range
% funName = @ackley_path; % fitness function name
% rang_l = -5; % left of dynamic range rastrigin
% rang_r = 5; % right of dynamic range
% minpts = 10;
max_iteration = 2000; % maximal number of iterations
XX=1;
%P = 0;
n_c = 2;
% P = 0.6;
% for P = 0:0.1:1
for idx = 1:30 % run times
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('bso.xls',fit,funStr, [str,'1']);
% output best fitness over generation to EXCEL worksheet for each BSO run
['run', num2str(idx)]
opt(idx,:)=fit; %可以把列换成行 fit为一列数据
% plot(x,opt(1,:)
end
opt;
format short g
best_value = min(opt(:,max_iteration))
worst_value = max(opt(:,max_iteration))
[ln col]=size(opt);
out_put(XX,:)=sum(opt)/ln; %每一列的均值
format short g
m_value = min(out_put(XX,:))
z = 0;
for idx = 1:30
z = z+(opt(idx,max_iteration)-m_value)^2;
end
SD = z/29
XX=XX+1
% end
% save UUB out_put(n_c,:);
% plot(out_put);
% end
% save UUB out_put(n_c,:);
% plot(opt);
figure(1)
save UUB out_put;
x=1:max_iteration;
% plot(x,out_put(1,:),'b',x,out_put(2,:),'g',x,out_put(3,:),'r',x,out_put(4,:),'c',x,out_put(5,:),'m',x,out_put(6,:),'y',x,out_put(7,:),'k',x,out_put(8,:),'b--',x,out_put(9,:),'g-.',);
% legend('k=2：蓝色','k=3：绿色','k=4：红色','k=5：青色','k=6：品红色','k=7：黄色','k=8：黑色','k=9','k=10');
plot(x,out_put(1,:))%,'b',x,out_put(2,:),'g',x,out_put(3,:),'r',x,out_put(4,:),'c',x,out_put(5,:),'m',x,out_put(6,:),'y',x,out_put(7,:),'k',x,out_put(8,:),'b--',x,out_put(9,:),'g-.',x,out_put(10,:),'r:',x,out_put(11,:),'c-.');
% legend('P=0','P=0.1','P=0.2','P=0.3','P=0.4','P=0.5','P=0.6','P=0.7','P=0.8','P=0.9','P=1');
toc
img =gcf; %获取当前画图的句柄
print(img, '-dpng', '-r600', './运行结果.png') %即可得到对应格式和期望dpi的图像
3 仿真结果
4 参考文献
[1]范虹, 史肖敏, and 姚若侠. "头脑风暴算法优化的乳腺MR图像软子空间聚类算法." 计算机科学与探索 8(2020):1348-1357.
5 MATLAB代码与数据下载地址
见博客主页
