Journal of Guangxi Normal University(Natural Science Edition) ›› 2021, Vol. 39 ›› Issue (4): 55-67.doi: 10.16088/j.issn.1001-6600.2020093002

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Grey Wolf Optimization Algorithm Based on Elite Learning for Nonlinear Parameters

LU Miao, HE Dengxu, QU Liangdong   

  1. College of Mathematics and Physics, Guangxi University for Nationalities, Nanning Guangxi 530006, China
  • Revised:2020-10-20 Online:2021-07-25 Published:2021-07-23

Abstract: In order to effectively improve the convergence speed and solution accuracy of grey wolf optimization algorithm, this paper combines the elite reverse learning strategy to increase the diversity of population, changes the convergence factor from linear to nonlinear, redesigns the position update formula to improve the convergence accuracy of the algorithm, and proposes an elite learning gray wolf optimization algorithm with nonlinear parameters. The experimental results of 8 groups of typical test functions show that the convergence speed and accuracy of the algorithm are improved to different degrees. When solving the optimization design problem of IIR digital filter, it shows excellent performance.

Key words: gray wolf optimization algorithm, elite opposition-based learning, nonlinear parameters, test function, IIR digital filter design

CLC Number: 

  • TP301.6
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