1 簡介

1.1 灰狼算法介紹

2 部分代碼

%___________________________________________________________________%% ?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 allclearclcAlgorithm_Name = 'I-GWO';N = 30; % Number of search agentsFunction_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 spacesubplot(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 spacesubplot(1,2,2);semilogy(Convergence_curve,'Color','r')title('Objective space')xlabel('Iteration');ylabel('Best score obtained so far');axis tightgrid onbox onlegend('I-GWO')

3 仿真結(jié)果

4 參考文獻(xiàn)

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