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An efficient and robust grey wolf optimizer algorithm for large-scale numerical optimization

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Abstract

Meta-heuristic algorithms are widely viewed as feasible techniques to solve continuous large-scale numerical optimization problems. Grey wolf optimizer (GWO) is a relatively new stochastic algorithm with only a few parameters to adjust that can be easily used for global optimization. This paper presents an efficient and robust GWO (ERGWO) variant to solve large-scale numerical optimization problems. Inspired by particle swarm optimization, a nonlinearly adjustment strategy for parameter control is designed to balance exploration and exploitation. Additionally, a modified position-updating equation is presented to improve convergence speed. The performance of ERGWO is verified on 18 benchmark large-scale numerical optimization problems with dimensions ranging from 30 to 10,000, 30 benchmarks from CEC 2014, 30 functions in CEC 2017, respectively. Numerical experiments are performed to compare ERGWO to the basic GWO algorithm, other GWO variants, and other well-known meta-heuristic search techniques. Simulations demonstrate that the proposed ERGWO algorithm can find high quality solutions with low computational cost and very fast convergence.

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Acknowledgements

The authors sincerely thank the anonymous associate editor and the four anonymous reviewers for providing detailed and valuable comments and suggestions that greatly helped us improve the quality of this paper. They also gratefully acknowledge Dr. Ming Xu for improving the presentation of this paper.

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61463009, the Program for the Science and Technology Top Talents of Higher Learning Institutions of Guizhou under Grant No. KY[2017]070, the Science and Technology Foundation of Guizhou Province under Grant No. [2016]1022, the Joint Foundation of Guizhou University of Finance and Economics and Ministry of Commerce under Grant No. 2016SWBZD13, the Education Department of Guizhou Province under Grant No. KY[2017]004, and the Project of High Level Creative Talents in Guizhou Province under Grant No. 20164035.

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Authors and Affiliations

  1. Key of Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang, 50025, People’s Republic of China

    Wen Long & Shaohong Cai

  2. School of Mathematics and Statistics, Guizhou University of Finance & Economics, Guiyang, 550025, People’s Republic of China

    Wen Long & Jianjun Jiao

  3. School of Energy Power and Engineering, Changsha University of Science & Technology, Changsha, 410114, People’s Republic of China

    Mingzhu Tang

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  1. Wen Long
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Correspondence to Shaohong Cai.

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Long, W., Cai, S., Jiao, J. et al. An efficient and robust grey wolf optimizer algorithm for large-scale numerical optimization. Soft Comput 24, 997–1026 (2020). https://doi.org/10.1007/s00500-019-03939-y

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