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Multi-Strategy Grey Wolf Optimization Algorithm for Global Optimization and Engineering Applications

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Abstract

The grey wolf optimizer(GWO), a population-based meta-heuristic algorithm, mimics the predatory behavior of grey wolf packs. Continuously exploring and introducing improvement mechanisms is one of the keys to drive the development and application of GWO algorithms. To overcome the premature and stagnation of GWO, the paper proposes a multiple strategy grey wolf optimization algorithm (MSGWO). Firstly, an variable weights strategy is proposed to improve convergence rate by adjusting the weights dynamically. Secondly, this paper proposes a reverse learning strategy, which randomly reverses some individuals to improve the global search ability. Thirdly, the chain predation strategy is designed to allow the search agent to be guided by both the best individual and the previous individual. Finally, this paper proposes a rotation predation strategy, which regards the position of the current best individual as the pivot and rotate other members for enhacing the exploitation ability. To verify the performance of the proposed technique, MSGWO is compared with seven state-of-the-art meta-heuristics and four variant GWO algorithms on CEC2022 benchmark functions and three engineering optimization problems. The results demonstrate that MSGWO has better performance on most of benchmark functions and shows competitive in solving engineering design problems.

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Data Availability

All data generated or analysed during this study are included in this published article (and its supplementary information files).

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Acknowledgments

The authors express sincerely appreciation to the anonymous reviewers for their helpful opinions. This work is supported by the National Natural Science Foundation of China under Grants Nos. 62006103 and 61872168, in part by the Postgraduate research and practice innovation program of Jiangsu Province under Grand No. KYCX24_3057, in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Normal University under Grand Nos. 2024XKT2643 and 2024XKT2642, in part by Xuzhou Basic Research Program under Grand No. KC23025, in part by the Royal Society International Exchanges Scheme IECNSFC211404 and in part by China Scholarship Council under Grand No. 202310090064.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221000, China

    Likai Wang, Qingyang Zhang & Yongquan Dong

  2. School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, Leicester, UK

    Shengxiang Yang

Authors
  1. Likai Wang
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Correspondence to Qingyang Zhang.

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Additional information

Likai Wang is currently a master’s student in the School of Computer Science and Technology at Jiangsu Normal University located in Jiangsu Province, China. His research areas include evolutionary algorithms, intelligent algorithms and their applications.

Qingyang Zhang received the Ph.D. degree from the School of Computer and Information, Hefei University of Technology, Hefei, China, in 2019. He is currently a Lecturer with the School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, China. His current research interests include dynamic optimization, evolutionary computation and image processing.

Shengxiang Yang (M’00–SM’14) received the Ph.D. degree from Northeastern University, Shenyang, China, in 1999. He is currently a Professor of Computational Intelligence and the Director of the Centre for Computational Intelligence, School of Computer Science and Informatics, De Montfort University, Leicester, U.K. He has over 270 publications with over 8500 Google Scholar citations and an H-index of 49. His current research interests include evolutionary computation, swarm intelligence, artificial neural networks, data mining and data stream mining and relevant real-world applications.

Yongquan Dong received the BS and PhD degrees in computer science from Shandong University. He is currently a professor in the School of Computer Science and Technology, Jiangsu Normal University, China. His research interests include web information integration and web data management.

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Wang, L., Zhang, Q., Yang, S. et al. Multi-Strategy Grey Wolf Optimization Algorithm for Global Optimization and Engineering Applications. J. Syst. Sci. Syst. Eng. (2024). https://doi.org/10.1007/s11518-024-5622-z

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