Coyote optimisation algorithm for separable nonlinear models using chaotic maps technique
by Xixi Ji; Jing Chen; Xia Yin
International Journal of Modelling, Identification and Control (IJMIC), Vol. 44, No. 4, 2024

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.

Online publication date: Thu, 13-Jun-2024

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