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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: 4625

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xiaoqing Zhang

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

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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.

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journal="Frontiers of Information Technology & Electronic Engineering",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000191"
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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

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