一、电鳗觅食优化算法
电鳗觅食优化算法(Electric eel foraging optimization,EEFO)由Weiguo Zhao等人提出的一种元启发算法,EEFO从自然界中电鳗表现出的智能群体觅食行为中汲取灵感。该算法对四种关键的觅食行为进行数学建模:相互作用、休息、狩猎和迁徙,以在优化过程中提供探索和利用。此外,还开发了一个能量因子来管理从全球搜索到本地搜索的过渡以及搜索空间中探索和开发之间的平衡。
参考文献:
Weiguo Zhao, Liying Wang, Zhenxing Zhang, Honggang Fan, Jiajie Zhang, Seyedali Mirjalili, Nima Khodadadi, Qingjiao Cao,Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications,Expert Systems with Applications,Volume 238, Part F,2024,122200,https://doi.org/10.1016/j.eswa.2023.122200.
二、23个函数介绍
参考文献:
[1] Yao X, Liu Y, Lin G M. Evolutionary programming made faster[J]. IEEE transactions on evolutionary computation, 1999, 3(2):82-102.
三、EEFO求解23个函数
3.1部分代码
close all ;
clear
clc
Npop=30;
Function_name='F1'; % Name of the test function that can be from F1 to F23 (
Tmax=300;
[lb,ub,dim,fobj]=Get_Functions_details(Function_name);
[Best_fit,Best_pos,Convergence_curve]=EEFO(Npop,Tmax,lb,ub,dim,fobj);
figure('Position',[100 100 660 290])
%Draw search space
subplot(1,2,1);
func_plot(Function_name);
title('Parameter space')
xlabel('x_1');
ylabel('x_2');
zlabel([Function_name,'( x_1 , x_2 )'])
%Draw objective space
subplot(1,2,2);
semilogy(Convergence_curve,'Color','r','linewidth',3)
title('Search space')
xlabel('Iteration');
ylabel('Best score obtained so far');
axis tight
grid on
box on
legend('EEFO')
saveas(gca,[Function_name '.jpg']);
display(['The best solution is ', num2str(Best_pos)]);
display(['The best fitness value is ', num2str(Best_fit)]);