Full Text:   <1317>

Summary:  <990>

Suppl. Mater.: 

CLC number: TP273+.1

On-line Access: 2018-01-12

Received: 2016-09-14

Revision Accepted: 2016-12-18

Crosschecked: 2017-11-26

Cited: 0

Clicked: 3254

Citations:  Bibtex RefMan EndNote GB/T7714


Xiao-Qing Zhang


-   Go to

Article info.
Open peer comments

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, Xi’an 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.

@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",
publisher="Zhejiang University Press & Springer",

%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
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601555

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

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

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


[1]Chaman-Motlagh, A., 2015. Superdefect photonic crystal filter optimization using grey wolf optimizer. IEEE Photon. Technol. Lett., 27(22):2355-2358.

[2]Emary, E., Zawbaa, H.M., 2016. Impact of chaos functions on modern swarm optimizers. PLoS ONE, 11(7):e0158738.

[3]Emary, E., Zawbaa, H.M., Hassanien, A.E., 2016. Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172:371-381.

[4]Gao, W.F., 2013. Artificial Bee Colony Algorithm and Its Applications. PhD Thesis, Xidian University, Xi’an, China (in Chinese).

[5]Gao, W.F., Liu, S.Y., Huang, L.L., 2012. Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique. Commun. Nonl. Sci. Numer. Simul., 17(11):4316-4327.

[6]Hadidian-Moghaddam, M.J., Arabi-Nowdeh, S., Bigdeli, M., 2016. Optimal sizing of a stand-alone hybrid photovoltaic/wind system using new grey wolf optimizer considering reliability. J. Renew. Sustain. Energy, 8:035903.

[7]Han, Z.M., Lin, Z.Y., Fu, M.Y., et al., 2015. Distributed coordination in multi-agent systems: a graph Laplacian perspective. Front. Inform. Technol. Electron. Eng., 16(6):429-448.

[8]Kamboj, V.K., 2016. A novel hybrid PSO-GWO approach for unit commitment problem. Neur. Comput. Appl., 27(6): 1643-1655.

[9]Kamboj, V.K., Bath, S.K., Dhillon, J.S., 2016. Solution of non-convex economic load dispatch problem using grey wolf optimizer. Neur. Comput. Appl., 27(5):1301-1316.

[10]Karaboga, D., 2005. An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report No. TR06, Erciyes University, Kayseri, Turkey.

[11]Komaki, G.M., Kayvanfar, V., 2015. Grey wolf optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time. J. Comput. Sci., 8:109-120.

[12]Korayem, L., Khorsid, M., Kassem, S.S., 2015. Using grey wolf algorithm to solve the capacitated vehicle routing problem. IOP Conf. Ser. Mater. Sci. Eng., 83:012014.

[13]Li, Z.C., Huang, X.L., 2016. Glowworm swarm optimization and its application to blind signal separation. Math. Probl. Eng., 2016:5481602.

[14]Liu, J.K., 2014. Intelligent Control (3rd Edition). Publishing House of Electronics Industry, Beijing, China, p.132-140 (in Chinese).

[15]Lu, C., Xiao, S.Q., Li, X.Y., 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.

[16]Mahdad, B., Srairi, K., 2015. Blackout risk prevention in a smart grid based flexible optimal strategy using grey wolf-pattern search algorithms. Energy Conv. Manag., 98:411-429.

[17]Medjahed, S.A., Saadi, T.A., Benyettou, A., et al., 2016. Gray wolf optimizer for hyperspectral band selection. Appl. Soft Comput., 40:178-186.

[18]Mirjalili, S., 2015. How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl. Intell., 43(1):150-161.

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

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

[21]Mohanty, S., Subudhi, B., Ray, P.K., 2016. A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions. IEEE Trans. Sustain. Energy, 7(1):181-188.

[22]Nabil, E., 2016. A modified flower pollination algorithm for global optimization. Expert Syst. Appl., 57:192-203.

[23]Oftadeh, R., Mahjoob, M.J., Shariatpanahi, M., 2010. A novel meta-heuristic optimization algorithm inspired by group hunting of animals: hunting search. Comput. Math. Appl., 60(7):2087-2098.

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

[25]Shakarami, M.R., Davoudkhani, I.F., 2016. Wide-area power system stabilizer design based on grey wolf optimization algorithm considering the time delay. Electr. Power Syst. Res., 133:149-159.

[26]Sharma, Y., Saikia, L.C., 2015. Automatic generation control of a multi-area ST-Thermal power system using grey wolf optimizer algorithm based classical controllers. Int. J. Electr. Power Energy Syst., 73:853-862.

[27]Storn, R., Price, K., 1997. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim., 11(4):341-359.

[28]Sulaiman, M.H., Mustaffa, Z., Mohamed, M.R., et al., 2015. Using the gray wolf optimizer for solving optimal reactive power dispatch problem. Appl. Soft Comput., 32:286-292.

[29]Thamaraiselvi, A., Santhi, R., 2016. A new approach for optimization of real life transportation problem in neutrosophic environment. Math. Probl. Eng., 2016:5950747.

[30]Venske, S.M., Gonçalves, R.A., Benelli, E.M., et al., 2016. ADEMO/D: an adaptive differential evolution for protein structure prediction problem. Expert Syst. Appl., 56:209-226.

[31]Wu, T.Q., Yao, M., Yang, J.H., 2016. Dolphin swarm algorithm. Front. Inform. Technol. Electron. Eng., 17(8):717-729.

[32]Yao, P., Wang, H.L., Ji, H.X., 2016. Multi-UAVs tracking target in urban environment by model predictive control and improved grey wolf optimizer. Aerosp. Sci. Technol., 55:131-143.

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

[34]Zhang, S., Zhou, Y.Q., Li, Z.M., et al., 2016. Grey wolf optimizer for unmanned combat aerial vehicle path planning. Adv. Eng. Softw., 99:121-136.

Open peer comments: Debate/Discuss/Question/Opinion


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 - Journal of Zhejiang University-SCIENCE