【智能優(yōu)化算法-灰狼算法】基于狩獵 (DLH) 搜索策略的灰狼算法求解單目標(biāo)優(yōu)化問題附matlab代碼
【智能優(yōu)化算法-灰狼算法】基于狩獵 (DLH) 搜索策略的灰狼算法求解單目標(biāo)優(yōu)化問題附matlab代碼
TT_Matlab
博主簡介：擅長智能優(yōu)化算法、神經(jīng)網(wǎng)絡(luò)預(yù)測、信號處理、元胞自動(dòng)機(jī)、圖像處理、路徑規(guī)劃、無人機(jī)等多種領(lǐng)域的Matlab仿真，完整matlab代碼或者程序定制加qq1575304183。
1 內(nèi)容介紹
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 仿真代碼
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Grey Wold Optimizer (GWO)
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codes version 1.0 %
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Developed
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Author and programmer: Seyedali Mirjalili %
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e-Mail: %
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seyedali.mirjalili@griffithuni.edu.au %
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Homepage: http://www.alimirjalili.com %
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Main paper: S. Mirjalili, S. M. Mirjalili, A. Lewis %
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Grey Wolf Optimizer, Advances
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Engineering %
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Software ,
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DOI: 10.1016/j.advengsoft.2013.12.007 %
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This
function
initialize the first population of search agents
function 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 lb
if Boundary_no==1
Positions=rand(SearchAgents_no,dim).*(ub-lb)+lb;
end
%
If each variable has a different lb and ub
if 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;
end
end
3 運(yùn)行結(jié)果
4 參考文獻(xiàn)
[1]唐宏偉. 未知環(huán)境下基于智能優(yōu)化算法的多機(jī)器人目標(biāo)搜索研究[D]. 湖南大學(xué).
[2]崔明朗, 杜海文, 魏政磊,等. 多目標(biāo)灰狼優(yōu)化算法的改進(jìn)策略研究[J]. 計(jì)算機(jī)工程與應(yīng)用, 2018, 54(5):9.