【灰狼算法】基于改进灰狼优化算法求解单目标优化问题附matlab代码
1 简介
1.1 灰狼算法介绍


2 部分代码
%___________________________________________________________________%
% An Improved Grey Wolf Optimizer for Solving Engineering %
% Problems (I-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
% N = 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 I-GWO: [Best_score,Best_pos,GWO_cg_curve]=IGWO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj)
%__________________________________________
close all
clear
clc
Algorithm_Name = 'I-GWO';
N = 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);
[Fbest,Lbest,Convergence_curve]=IGWO(dim,N,Max_iteration,lb,ub,fobj);
display(['The best solution obtained by I-GWO is : ', num2str(Lbest)]);
display(['The best optimal value of the objective funciton found by I-GWO is : ', num2str(Fbest)]);
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(Convergence_curve,'Color','r')
title('Objective space')
xlabel('Iteration');
ylabel('Best score obtained so far');
axis tight
grid on
box on
legend('I-GWO')
3 仿真结果


4 参考文献
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