Title: A novel chaotic grey wolf optimisation for high-dimensional and numerical optimisation

Authors: Meng Jian Zhang; Dao Yin Long; Dan Dan Li; Xiao Wang; Tao Qin; Jing Yang

Addresses: Electrical Engineering College, Guizhou University, Guiyang 550025, Guizhou, China ' Power China Guizhou Engineering Co., Ltd., Guizhou University of Technology, Guiyang 550001, Guizhou, China ' Electrical Engineering College, Guizhou University, Guiyang 550025, Guizhou, China ' Electrical Engineering College, Guizhou University, Guiyang 550025, Guizhou, China ' Electrical Engineering College, Guizhou University, Guiyang 550025, Guizhou, China ' Electrical Engineering College, Guizhou University, Guiyang 550025, Guizhou, China

Abstract: Aiming at the weakness of the current evolutionary algorithms for high-dimensional and numerical optimisation problems of global convergence, a novel chaotic grey wolf optimisation (NCGWO) is proposed for solving the high-dimensional optimisation problems. Firstly, the six chaotic one-dimensional maps are introduced and their mathematical models are improved with their mapping ranges being in the interval (0, 1). Secondly, the diversity experiments are conducted to test the results of the chaotic maps. The experiments show that the initial population by chaotic maps is superior to the GWO algorithm and the Sine map is best. Finally, the CSGWO algorithm is also proposed based on the NCGWO algorithm with the parameter C by Sine map. The simulations demonstrate that the performance of the GWO algorithm can be improved by the chaotic maps for high-dimensional and numerical optimisation problems, and the effectiveness of the CSGWO algorithm is superior to other evolutionary algorithms and achieves better accuracy and convergence speed.

Keywords: chaotic system; GWO; grey wolf optimisation; chaos initialisation; optimisation; high-dimension.

DOI: 10.1504/IJCAT.2021.121524

International Journal of Computer Applications in Technology, 2021 Vol.67 No.2/3, pp.194 - 203

Received: 13 Oct 2020
Accepted: 06 Jan 2021
Published online: 17 Mar 2022 *

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