Title: Coyote optimisation algorithm for separable nonlinear models using chaotic maps technique

Authors: Xixi Ji; Jing Chen; Xia Yin

Addresses: School of Science, Jiangnan University, Wuxi 214122, China ' The Science and Technology on Near-Surface Detection Laboratory, Jiangnan University, Wuxi 214122, China; School of Science, Jiangnan University, Wuxi 214122, China ' School of Science, Jiangnan University, Wuxi 214122, China

Abstract: In this paper, a new Chebyshev chaotic map-based chaotic coyote optimisation algorithm (CCOA) is applied to identify a separable nonlinear model. The CCOA uses chaotic signals instead of random numbers in the identification process to increase non-repetitiveness and ergodicity. Compared with the particle swarm optimisation (PSO) and coyote optimisation algorithm (COA), the CCOA improves the estimation accuracy and the parameter estimation convergence rate. To validate the developed algorithm, a series of comparative experiments are conducted. The effectiveness of the proposed algorithm is verified by the simulation results.

Keywords: chaotic coyote optimisation algorithm; CCOA; chaotic signal; separable nonlinear model; parameter estimation.

DOI: 10.1504/IJMIC.2024.139095

International Journal of Modelling, Identification and Control, 2024 Vol.44 No.4, pp.360 - 367

Received: 22 Jan 2023
Accepted: 13 Apr 2023
Published online: 13 Jun 2024 *

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