Full Text:   <1316>

Summary:  <948>

CLC number: TP301

On-line Access: 2021-07-12

Received: 2020-04-24

Revision Accepted: 2020-07-09

Crosschecked: 2021-05-17

Cited: 0

Clicked: 2382

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xiaoqing Zhang

https://orcid.org/0000-0001-8939-2570

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.6 P.877-890

http://doi.org/10.1631/FITEE.2000191


Improved dynamic grey wolf optimizer


Author(s):  Xiaoqing Zhang, Yuye Zhang, Zhengfeng Ming

Affiliation(s):  School of Physics and Electronic Engineering, Xianyang Normal University, Xianyang 712000, China; more

Corresponding email(s):   249140543@qq.com

Key Words:  Swarm intelligence, Grey wolf optimizer, Dynamic grey wolf optimizer, Optimization experiment


Xiaoqing Zhang, Yuye Zhang, Zhengfeng Ming. Improved dynamic grey wolf optimizer[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(6): 877-890.

@article{title="Improved dynamic grey wolf optimizer",
author="Xiaoqing Zhang, Yuye Zhang, Zhengfeng Ming",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="6",
pages="877-890",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000191"
}

%0 Journal Article
%T Improved dynamic grey wolf optimizer
%A Xiaoqing Zhang
%A Yuye Zhang
%A Zhengfeng Ming
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 6
%P 877-890
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000191

TY - JOUR
T1 - Improved dynamic grey wolf optimizer
A1 - Xiaoqing Zhang
A1 - Yuye Zhang
A1 - Zhengfeng Ming
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 6
SP - 877
EP - 890
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000191


Abstract: 
In the standard grey wolf optimizer (GWO), the search wolf must wait to update its current position until the comparison between the other search wolves and the three leader wolves is completed. During this waiting period, the standard GWO is seen as the static GWO. To get rid of this waiting period, two dynamic GWO algorithms are proposed: the first dynamic grey wolf optimizer (DGWO1) and the second dynamic grey wolf optimizer (DGWO2). In the dynamic GWO algorithms, the current search wolf does not need to wait for the comparisons between all other search wolves and the leading wolves, and its position can be updated after completing the comparison between itself or the previous search wolf and the leading wolves. The position of the search wolf is promptly updated in the dynamic GWO algorithms, which increases the iterative convergence rate. Based on the structure of the dynamic GWOs, the performance of the other improved GWOs is examined, verifying that for the same improved algorithm, the one based on dynamic GWO has better performance than that based on static GWO in most instances.

改进的动态灰狼优化算法

张小青1,2,张玉叶1,明正峰2
1咸阳师范学院物理与电子工程学院,中国咸阳市,712000
2西安电子科技大学机电工程学院,中国西安市,710071
摘要:在标准灰狼优化算法(GWO)中,搜索狼必须等到其他搜索狼与3个领导狼之间的比较完成后才能更新其当前位置矢量。正因为有此等待时间,标准GWO被视为静态GWO。为消除这种等待时间,提出两种动态GWO算法:第一种动态灰狼优化算法(DGWO1)和第二种动态灰狼优化算法(DGWO2)。在动态GWO算法中,当前搜索狼不需要等待所有其他搜索狼与领导狼的比较,在完成自身或前一匹搜索狼与领导狼的比较后,即可更新其位置矢量。动态GWO算法及时更新搜索狼的位置,提高了算法迭代收敛速度。以动态GWO算法结构为基础,对其他改进GWO算法也进行了一定的性能测验。实验证明,对同一改进GWO算法,以动态GWO结构为基础时的性能总体上优于以静态GWO结构为基础时的性能。

关键词:群智能;灰狼优化算法;动态灰狼优化算法;优化实验

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Al-Betar MA, Awadallah MA, Faris H, et al., 2018. Natural selection methods for grey wolf optimizer. Expert Syst Appl, 113:481-499.

[2]Cong SL, Sun J, Mao HP, et al., 2018. Non-destructive detection for mold colonies in rice based on hyperspectra and GWO-SVR. J Sci Food Agric, 98(4):1453-1459.

[3]Daniel E, 2018. Optimum wavelet based homomorphic medical image fusion using hybrid genetic-grey wolf optimization algorithm. IEEE Sens J, 18(6):6804-6811.

[4]Emary E, Zawbaa HM, Hassanien AE, 2016. Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172:371-381.

[5]Gupta S, Deep K, 2018. Cauchy grey wolf optimiser for continuous optimisation problems. J Exp Theor Artif Intell, 30(6):1051-1075.

[6]Gupta S, Deep K, 2019a. Hybrid grey wolf optimizer with mutation operator. In: Bansal JC, Das KN, Nagar A, et al. (Eds.), Soft Computing for Problem Solving. Springer, Singapore, p.961-968.

[7]Gupta S, Deep K, 2019b. A novel random walk grey wolf optimizer. Swarm Evol Comput, 44:101-112.

[8]Gupta S, Deep K, 2019c. An opposition-based chaotic grey wolf optimizer for global optimisation tasks. J Exp Theor Artif Intelll, 31(5):751-779.

[9]Gupta S, Deep K, 2020. A memory-based grey wolf optimizer for global optimization tasks. Appl Soft Comput, 93: 106367.

[10]Gupta S, Deep K, Moayedi H, et al., 2020. Sine cosine grey wolf optimizer to solve engineering design problems. Eng Comput, online.

[11]Liu XL, Tian Y, Lei XH, et al., 2019. An improved self-adaptive grey wolf optimizer for the daily optimal operation of cascade pumping stations. Appl Soft Comput, 75:473-493.

[12]Long W, Jiao JJ, Liang XM, et al., 2018. An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization. Eng Appl Artif Intell, 68:63-80.

[13]Lu C, Xiao SQ, Li XY, et al., 2016. An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production. Adv Eng Softw, 99:161-176.

[14]Mirjalili S, Mirjalili SM, Lewis A, 2014. Grey wolf optimizer. Adv Eng Softw, 69:46-61.

[15]Mirjalili S, Saremi S, Mirjalili SM, et al., 2016. Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl, 47:106-119.

[16]Qais MH, Hasanien HM, Alghuwainem S, 2018. Augmented grey wolf optimizer for grid-connected PMSG-based wind energy conversion systems. Appl Soft Comput, 69: 504-515.

[17]Rodríguez L, Castillo O, Soria J, et al., 2017. A fuzzy hierarchical operator in the grey wolf optimizer algorithm. Appl Soft Comput, 57:315-328.

[18]Sahoo BP, Panda S, 2018. Improved grey wolf optimization technique for fuzzy aided PID controller design for power system frequency control. Sustain Energy Grids Netw, 16:278-299.

[19]Saremi S, Mirjalili SZ, Mirjalili SM, 2015. Evolutionary population dynamics and grey wolf optimizer. Neur Comput Appl, 26(5):1257-1263.

[20]Saxena A, Kumar R, Das S, 2019. β-Chaotic map enabled grey wolf optimizer. Appl Soft Comput, 75:84-105.

[21]Tripathi AK, Sharma K, Bala M, 2018. A novel clustering method using enhanced grey wolf optimizer and MapReduce. Big Data Res, 14:93-100.

[22]Wu GH, Mallipeddi R, Suganthan PN, 2016. Problem Definitions and Evaluation Criteria for the CEC 2017 Competition and Special Session on Constrained Single Objective Real-Parameter Optimization. Technical Report, No. 201212, Nanyang Technological University, Singapore.

[23]Zawbaa HM, Emary E, Grosan C, et al., 2018. Large-dimensionality small-instance set feature selection: a hybrid bio-inspired heuristic approach. Swarm Evol Comput, 42:29-42.

[24]Zhang S, Zhou YQ, 2015. Grey wolf optimizer based on Powell local optimization method for clustering analysis. Discr Dynam Nat Soc, 2015:481360.

[25]Zhang XM, Kang Q, Cheng JF, et al., 2018. A novel hybrid algorithm based on biogeography-based optimization and grey wolf optimizer. Appl Soft Comput, 67:197-214.

[26]Zhang XQ, Ming ZF, 2017. An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application. Front Inform Technol Electron Eng, 18(11):1705-1719.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2022 Journal of Zhejiang University-SCIENCE