1 简介
乌燕鸥优化算法是由 G. Dhiman 和 A.Kaur于 2019 年针对工业工程问题提出的一种新的优化算法，其灵感来源于海鸟在自然界中觅食的行为，乌燕鸥是杂食性鸟类,以蚯蚓、昆虫、鱼等食物为生。
This paper presents a bio-inspired algorithm called Sooty Tern Optimization Algorithm (STOA) for solvingconstrained industrial problems. The main inspiration of this algorithm is the migration and attacking behaviorsof sea bird sooty tern in nature. These two steps are implemented and mathematically modeled to emphasizeexploitation and exploration in a given search space. The proposed algorithm is compared with nine wellknown bio-inspired algorithms over 44 benchmark test functions. The analysis of convergence behaviors andcomputational complexity of the proposed algorithm have been evaluated. Furthermore, to demonstrate itsapplicability it is then employed to solve six constrained industrial applications. The outcomes of experimentreveal that the proposed algorithm is able to solve challenging constrained problems and is very competitivecompared with other optimization algorithms.
2 部分代码
%################################################################################################%
%
%################################################################################################%
clear all
clc
SearchAgents=30;
Fun_name='F2';
Max_iterations=1000;
[lowerbound,upperbound,dimension,fitness]=fun_info(Fun_name);
[Best_score,Best_pos,STOA_curve]=stoa(SearchAgents,Max_iterations,lowerbound,upperbound,dimension,fitness);
figure('Position',[500 500 660 290])
subplot(1,2,1);
func_plot(Fun_name);
title('Objective space')
xlabel('x_1');
ylabel('x_2');
zlabel([Fun_name,'( x_1 , x_2 )'])
subplot(1,2,2);
plots=semilogx(STOA_curve,'Color','r');
set(plots,'linewidth',2)
hold on
title('Objective space')
xlabel('Iterations');
ylabel('Best score');
axis tight
grid on
box on
legend('STOA')
display(['The best solution obtained by STOA is : ', num2str(Best_pos)]);
display(['The best optimal value of the objective funciton found by STOA : ', num2str(Best_score)]);
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
Jia He-Ming, Li Yao, Sun Kang-Jian. Simultaneous feature selection optimization based on hybrid sooty tern optimization algorithm and genetic algorithm. Acta Automatica Sinica, 2020, 46(x): 1−15 doi: 10.16383/j.aas.c200322
部分理论引用网络文献，若有侵权联系博主删除。
5 MATLAB代码与数据下载地址
见博客主页
