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CLC number: TP273+.1

On-line Access: 2018-01-12

Received: 2016-09-14

Revision Accepted: 2016-12-18

Crosschecked: 2017-11-26

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Citations:  Bibtex RefMan EndNote GB/T7714


Xiao-Qing Zhang


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.11 P.1705-1719


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

Author(s):  Xiao-Qing Zhang, Zheng-Feng Ming

Affiliation(s):  School of Mechano-Electronic Engineering, Xidian University, Xian 710071, China; more

Corresponding email(s):   249140543@qq.com

Key Words:  Swarm intelligence, Grey wolf optimizer, Optimization, Radial basis function network

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.

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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.


概要: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a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。

关键词:群智能;苍狼优化算法;优化;RBF(radial basis function)网络

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