?1 教與學(xué)的優(yōu)化算法

簡(jiǎn)要分析了群智能優(yōu)化算法的研究現(xiàn)狀,重點(diǎn)對(duì)"教與學(xué)"優(yōu)化算法作了詳細(xì)的描述,并分析了"教與學(xué)"算法的性能及其優(yōu)缺點(diǎn);隨后介紹了幾種改進(jìn)的"教與學(xué)"優(yōu)化算法,對(duì)"教與學(xué)"優(yōu)化算法的應(yīng)用研究情況進(jìn)行了論述。最后,說(shuō)明了目前"教與學(xué)"優(yōu)化算法中存在的問題,并指出"教與學(xué)"優(yōu)化算法未來(lái)的研究方向。

2 部分代碼

```matlab

clear all?

clc

Population=40; % Number of Popultaion

Function_name='F12'; % Name of the test function that can be from F1 to F23?

Max_iteration=50; % Maximum numbef of iterations

% Load details of the selected benchmark function

[lb,ub,dim,fobj]=Get_Functions_details(Function_name);

[Best_pos,Best_score,cg_curve,P,f] = TLBO(fobj,lb,ub,dim,Population,Max_iteration);

figure('Position',[500 500 660 290])

%Draw search space

subplot(1,2,1);

func_plot(Function_name);

title('Test function')

xlabel('x_1');

ylabel('x_2');

zlabel([Function_name,'( x_1 , x_2 )'])

grid off

%Draw objective space

subplot(1,2,2);

plot(cg_curve,'Color','r')

title('Convergence curve')

xlabel('Iteration');

ylabel('Best score obtained so far');

axis tight

grid off

box on

legend('TLBO')

display(['The best solution obtained by TLBO is : ', num2str(Best_pos)]);

display(['The best optimal value of the objective funciton found by TLBO is : ', num2str(Best_score)]);

```

3 仿真結(jié)果

4 參考文獻(xiàn)

[1]拓守恒, 雍龍泉, 鄧方安. “教與學(xué)”優(yōu)化算法研究綜述[J]. 計(jì)算機(jī)應(yīng)用研究, 2013.