CLC number: TP273+.1
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-11-26
Cited: 0
Clicked: 6485
Xiao-Qing Zhang , Zheng-Feng Ming . An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(11): 1705-1719.
@article{title="An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application",
author="Xiao-Qing Zhang , Zheng-Feng Ming ",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="11",
pages="1705-1719",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601555"
}
%0 Journal Article
%T An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application
%A Xiao-Qing Zhang
%A Zheng-Feng Ming
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 11
%P 1705-1719
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%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601555
TY - JOUR
T1 - An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application
A1 - Xiao-Qing Zhang
A1 - Zheng-Feng Ming
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 11
SP - 1705
EP - 1719
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601555
Abstract: Due to its simplicity and ease of use, the standard grey wolf optimizer (GWO) is attracting much attention. However, due to its imperfect search structure and possible risk of being trapped in local optima, its application has been limited. To perfect the performance of the algorithm, an optimized GWO is proposed based on a mutation operator and eliminating-reconstructing mechanism (MR-GWO). By analyzing GWO, it is found that it conducts search with only three leading wolves at the core, and balances the exploration and exploitation abilities by adjusting only the parameter a, which means the wolves lose some diversity to some extent. Therefore, a mutation operator is introduced to facilitate better searching wolves, and an eliminating-reconstructing mechanism is used for the poor search wolves, which not only effectively expands the stochastic search, but also accelerates its convergence, and these two operations complement each other well. To verify its validity, MR-GWO is applied to the global optimization experiment of 13 standard continuous functions and a radial basis function (RBF) network approximation experiment. Through a comparison with other algorithms, it is proven that MR-GWO has a strong advantage.
The online version of this article contains electronic supplementary materials, which are available to authorized users.
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