【智能优化算法-灰狼算法】基于狩猎 (DLH) 搜索策略的灰狼算法求解单目标优化问题附matlab代码

1 内容介绍

Grey wolf optimization (GWO) algorithm is a new emerging algorithm that is based on the social hierarchy of grey wolves as well as their hunting and cooperation strategies. Introduced in 2014, this algorithm has been used by a large number of researchers and designers, such that the number of citations to the original paper exceeded many other algorithms. In a recent study by Niu et al., one of the main drawbacks of this algorithm for optimizing real﹚orld problems was introduced. In summary, they showed that GWO's performance degrades as the optimal solution of the problem diverges from 0. In this paper, by introducing a straightforward modification to the original GWO algorithm, that is, neglecting its social hierarchy, the authors were able to largely eliminate this defect and open a new perspective for future use of this algorithm. The efficiency of the proposed method was validated by applying it to benchmark and real﹚orld engineering problems.

2 仿真代码

%___________________________________________________________________%%  Grey Wold Optimizer (GWO) source codes version 1.0               %%                                                                   %%  Developed in MATLAB R2011b(7.13)                                 %%                                                                   %%  Author and programmer: Seyedali Mirjalili                        %%                                                                   %%         e-Mail: ali.mirjalili@gmail.com                           %%                 seyedali.mirjalili@griffithuni.edu.au             %%                                                                   %%       Homepage: http://www.alimirjalili.com                       %%                                                                   %%   Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis             %%               Grey Wolf Optimizer, Advances in Engineering        %%               Software , in press,                                %%               DOI: 10.1016/j.advengsoft.2013.12.007               %%                                                                   %%___________________________________________________________________%​% This function initialize the first population of search agentsfunction Positions=initialization(SearchAgents_no,dim,ub,lb)​Boundary_no= size(ub,2); % numnber of boundaries​% If the boundaries of all variables are equal and user enter a signle% number for both ub and lbif Boundary_no==1    Positions=rand(SearchAgents_no,dim).*(ub-lb)+lb;end​% If each variable has a different lb and ubif Boundary_no>1    for i=1:dim        ub_i=ub(i);        lb_i=lb(i);        Positions(:,i)=rand(SearchAgents_no,1).*(ub_i-lb_i)+lb_i;    endend

3 运行结果

4 参考文献

[1]唐宏伟. 未知环境下基于智能优化算法的多机器人目标搜索研究[D]. 湖南大学.

[2]崔明朗, 杜海文, 魏政磊,等. 多目标灰狼优化算法的改进策略研究[J]. 计算机工程与应用, 2018, 54(5):9.

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