An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application<sup>#</sup>

Xiao-qing ZHANG; Zheng-feng MING

doi:10.1631/FITEE.1601555

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An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application^#

Regular Papers | Updated：2022-05-19

- An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application^#
- 一种基于变异算子与淘汰重组机制的改进GWO及其应用
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 11, Pages: 1705-1719(2017)
- Affiliations：
  
  School of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, China
  School of Physics and Electronic Engineering, Xianyang Normal University, Xianyang 712000, China
- Author bio：
  
  Xiao-qing ZHANG, E-mail: 249140543@qq.com
- Funds：
- DOI：10.1631/FITEE.1601555
  CLC： TP273.1
- Received：14 September 2016，
  
  Revised：2017-11-26，
  
  Published：2017-11
- Accepted：
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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.
DOI：

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. DOI： 10.1631/FITEE.1601555.

摘要

标准苍狼优化算法（grey wolf optimizer

GWO）因其简单易用的特性受到广泛关注。由于存在搜索结构不完善、易陷入局部最优等问题，其应用范围受到了限制。本文提出了一种基于变异算子和淘汰重组机制的苍狼优化算法（eliminating-reconstructing GWO

MR-GWO）。对GWO的分析表明，GWO仅以三个领导层苍狼为核心进行搜索，且仅通过调整参数

来平衡算法的探索和开发性能，意味着苍狼群在一定程度上失去了多样性。因此，本文对优秀的搜索狼引入变异算子，对性能较差的搜索狼采用淘汰重组机制，不仅有效地扩展了算法的随机搜索面，同时加快了算法收敛速度。为了验证改进后算法的有效性，通过13个标准连续函数全局优化实验及RBF（radial basis function）网络逼近试验将MR-GWO算法与其它算法进行了比较，试验结果表明MR-GWO算法具有较强的竞争力。

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

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.

关键词

Keywords

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