1 简介
Mirjalili 等人提出了一种新的群体智能算法———灰狼优化算法(GWO)，并通过多个基准测试函数进行测试，从结果上验证了该算法的可行性，通过对比，GWO 算法已被证明在算法对函数求解精度和稳定性上要明显优于 PSO、DE 和 GSA 算 法。
生物在自然界严酷环境下，即使并不具有人类的高智能，但在相同的目标，即食物的激励下，通过不断地适应与集体合作都表现出了令人惊叹的群体智能。文献［6］基于狼群严密的组织系统及其精妙的协作捕猎方式，提出了一种新的群体智能算法———灰狼优化算法。
2 部分代码
%___________________________________________________________________%
% Grey Wolf Optimizer (GWO) source codes version 1.0 %
% %
% You can simply define your cost in a seperate file and load its handle to fobj
% The initial parameters that you need are:
%__________________________________________
% fobj = @YourCostFunction
% dim = number of your variables
% Max_iteration = maximum number of generations
% SearchAgents_no = number of search agents
% lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n
% ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n
% If all the variables have equal lower bound you can just
% define lb and ub as two single number numbers
% To run GWO: [Best_score,Best_pos,GWO_cg_curve]=GWO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj)
%__________________________________________
clear all
clc
SearchAgents_no=30; % Number of search agents
Function_name='F2'; % Name of the test function that can be from F1 to F23 (Table 1,2,3 in the paper)
Max_iteration=500; % Maximum numbef of iterations
% Load details of the selected benchmark function
[lb,ub,dim,fobj]=Get_Functions_details(Function_name);
[Best_score,Best_pos,GWO_cg_curve]=GWO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj);
figure('Position',[500 500 660 290])
%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(GWO_cg_curve,'Color','r')
title('Objective space')
xlabel('Iteration');
ylabel('Best score obtained so far');
axis tight
grid on
box on
legend('GWO')
display(['The best solution obtained by GWO is : ', num2str(Best_pos)]);
display(['The best optimal value of the objective funciton found by GWO is : ', num2str(Best_score)]);
3 仿真结果
4 参考文献
[1]尹元亚, 蒋国臻, 田佳, 张钰, 何民, & 王昕. (). 基于灰狼优化算法的火电机组负荷优化分配经济性研究. 电气自动化, 42(1), 4.
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
