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.
Similar content being viewed by others
References
Chaman-Motlagh, A., 2015. Superdefect photonic crystal filter optimization using grey wolf optimizer. IEEE Photon. Technol. Lett., 27(22): 2355–2358. https://doi.org/10.1109/LPT.2015.2464332
Emary, E., Zawbaa, H.M., 2016. Impact of chaos functions on modern swarm optimizers. PLoS ONE, 11(7): e0158738. https://doi.org/10.1371/journal.pone.0158738
Emary, E., Zawbaa, H.M., Hassanien, A.E., 2016. Binary grey wolf optimization approaches for feature selection. Neu-rocomputing, 172: 371–381. https://doi.org/10.1016/j.neucom.2015.06.083
Gao, W.F., 2013. Artificial Bee Colony Algorithm and Its Applications. PhD Thesis, Xidian University, Xi’an, China (in Chinese).
Gao, W.F., Liu, S.Y., Huang, L.L., 2012. Particle swarm op-timization with chaotic opposition-based population ini-tialization and stochastic search technique. Commun. Nonl. Sci. Numer. Simul., 17(11): 4316–4327. https://doi.org/10.1016/j.cnsns.2012.03.015
Hadidian-Moghaddam, M.J., Arabi-Nowdeh, S., Bigdeli, M., 2016. Optimal sizing of a stand-alone hybrid photovol-taic/wind system using new grey wolf optimizer consid-ering reliability. J. Renew. Sustain. Energy, 8: 035903. https://doi.org/10.1063/1.4950945
Han, Z.M., Lin, Z.Y., Fu, M.Y., et al., 2015. Distributed co-ordination in multi-agent systems: a graph Laplacian perspective. Front. Inform. Technol. Electron. Eng., 16(6): 429–448. https://doi.org/10.1631/FITEE.1500118
Kamboj, V.K., 2016. A novel hybrid PSO-GWO approach for unit commitment problem. Neur. Comput. Appl., 27(6): 1643–1655. https://doi.org/10.1007/s00521-015-1962-4
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. https://doi.org/10.1007/s00521-015-1934-8
Karaboga, D., 2005. An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report No. TR06, Erciyes University, Kayseri, Turkey.
Komaki, G.M., Kayvanfar, V., 2015. Grey wolf optimizer algorithm for the two-stage assembly flow shop sched-uling problem with release time. J. Comput. Sci., 8: 109–120. https://doi.org/10.1016/j.jocs.2015.03.011
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. https://doi.org/10.1088/1757-899X/83/1/012014
Li, Z.C., Huang, X.L., 2016. Glowworm swarm optimization and its application to blind signal separation. Math. Probl. Eng., 2016: 5481602. https://doi.org/10.1155/2016/5481602
Liu, J.K., 2014. Intelligent Control (3rd Ed.). Publishing House of Electronics Industry, Beijing, China, p.132–140 (in Chinese).
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. https://doi.org/10.1016/j.advengsoft.2016.06.004
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. https://doi.org/10.1016/j.enconman.2015.04.005
Medjahed, S.A., Saadi, T.A., Benyettou, A., et al., 2016. Gray wolf optimizer for hyperspectral band selection. Appl. Soft Comput., 40: 178–186. https://doi.org/10.1016/j.asoc.2015.09.045
Mirjalili, S., 2015. How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl. Intell., 43(1): 150–161. https://doi.org/10.1007/s10489-014-0645-7
Mirjalili, S., Mirjalili, S.M., Lewis, A., 2014. Grey wolf op-timizer. Adv. Eng. Softw., 69: 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
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. https://doi.org/10.1016/j.eswa.2015.10.039
Mohanty, S., Subudhi, B., Ray, P.K., 2016. A new MPPT design using grey wolf optimization technique for pho-tovoltaic system under partial shading conditions. IEEE Trans. Sustain. Energy, 7(1): 181–188. https://doi.org/10.1109/TSTE.2015.2482120
Nabil, E., 2016. A modified flower pollination algorithm for global optimization. Expert Syst. Appl., 57: 192–203. https://doi.org/10.1016/j.eswa.2016.03.047
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. https://doi.org/10.1016/j.camwa.2010.07.049
Saremi, S., Mirjalili, S.Z., Mirjalili, S.M., 2015. Evolutionary population dynamics and grey wolf optimizer. Neur. Comput. Appl., 26(5): 1257–1263. https://doi.org/10.1007/s00521-014-1806-7
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. https://doi.org/10.1016/j.epsr.2015.12.019
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. https://doi.org/10.1016/j.ijepes.2015.06.005
Storn, R., Price, K., 1997. Differential evolution—a simple and efficient heuristic for global optimization over con-tinuous spaces. J. Glob. Optim., 11(4): 341–359. https://doi.org/10.1023/A:1008202821328
Sulaiman, M.H., Mustaffa, Z., Mohamed, M.R., et al., 2015. Using the gray wolf optimizer for solving optimal reac-tive power dispatch problem. Appl. Soft Comput., 32: 286–292. https://doi.org/10.1016/j.asoc.2015.03.041
Thamaraiselvi, A., Santhi, R., 2016. A new approach for op-timization of real life transportation problem in neutro-sophic environment. Math. Probl. Eng., 2016: 5950747. https://doi.org/10.1155/2016/5950747
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. https://doi.org/10.1016/j.eswa.2016.03.009
Wu, T.Q., Yao, M., Yang, J.H., 2016. Dolphin swarm algo-rithm. Front. Inform. Technol. Electron. Eng., 17(8): 717–729. https://doi.org/10.1631/FITEE.1500287
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. https://doi.org/10.1016/j.ast.2016.05.016
Zhang, S., Zhou, Y.Q., 2015. Grey wolf optimizer based on Powell local optimization method for clustering analysis. Discr. Dynam. Nat. Soc., 2015: 481360. https://doi.org/10.1155/2015/481360
Zhang, S., Zhou, Y.Q., Li, Z.M., et al., 2016. Grey wolf op-timizer for unmanned combat aerial vehicle path planning. Adv. Eng. Softw., 99: 121–136. https://doi.org/10.1016/j.advengsoft.2016.05.015
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the National High-Tech R&D Program (863) of China (No. 2015AA7041003), the Scientific Research Plan Projects of Shanxi Education Department (No. 17JK0825), and the Scientific Research Plan Projects of Xianyang Normal University (No. 15XSYK036)
Electronic supplementary material
11714_2017_1172_MOESM1_ESM.pdf
An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application
Rights and permissions
About this article
Cite this article
Zhang, Xq., Ming, Zf. An optimized grey wolf optimizer based on a mutation operator and eliminating-reconstructing mechanism and its application. Frontiers Inf Technol Electronic Eng 18, 1705–1719 (2017). https://doi.org/10.1631/FITEE.1601555
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.1601555