1 简介
秃鹰搜索 (bald eagle search,BES) 优化是马来西亚学者Alsattar 于2020年提出的一种新型元启发式算法, 该算法具有较强的全局搜索能力, 能够有效地解决各类复杂数值优化问题的优点
This study proposes a bald eagle search (BES) algorithm, which is a novel, nature-inspired meta-heuristic optimisation algorithm that mimics the hunting strategy or intelligent social behaviour of bald eagles as they search for fish. Hunting by BES is divided into three stages. In the first stage (selecting space), an eagle selects the space with the most number of prey. In the second stage (searching in space), the eagle moves inside the selected space to search for prey. In the third stage (swooping), the eagle swings from the best position identified in the second stage and determines the best point to hunt. Swooping starts from the best point and all other movements are directed towards this point. BES is tested by adopting a three-part evaluation methodology that (1) describes the benchmarking of the optimisation problem to evaluate the algorithm performance, (2) compares the algorithm performance with that of other intelligent computation techniques and parameter settings and (3) evaluates the algorithm based on mean, standard deviation, best point and Wilcoxon signed-rank test statistic of the function values. Optimisation results and discussion confirm that the BES algorithm
2 部分代码
clc;
clear;
number_fun='F2';
MaxIt=1000;
nPop=80;
[low,high,dim,fun] = Get_Functions_details(number_fun);
[value,fun_hist]=BES(nPop,MaxIt,low,high,dim,fun);
% plot(fun_hist,'-','Linewidth',1.5)
% xlabel('Iteration')
% ylabel('fitness')
% legend('BES')
figure('Position',[500 500 660 290])
subplot(1,2,1);
func_plot(number_fun);
title('Objective space')
xlabel('x_1');
ylabel('x_2');
zlabel([number_fun,'( x_1 , x_2 )'])
subplot(1,2,2);
plots=semilogx(fun_hist,'Color','r');
set(plots,'linewidth',2)
hold on
title('Objective space')
xlabel('Iterations');
ylabel('Best score');
3 仿真结果
4 参考文献
[1]贾鹤鸣,姜子超,李瑶.基于改进秃鹰搜索算法的同步优化特征选择[J/OL].控制与决策:1-9[2021-02-09].
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
