A modified Salp Swarm Algorithm for parameter estimation of fractional-order chaotic systems

Qingwen Cai (), Renhuan Yang, Chao Shen, Kelong Yue and Yibin Chen
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Qingwen Cai: College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China
Renhuan Yang: College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China
Chao Shen: College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China
Kelong Yue: College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China
Yibin Chen: College of Information Science and Technology, Jinan University, Guangzhou 510632, P. R. China

International Journal of Modern Physics C (IJMPC), 2023, vol. 34, issue 10, 1-15

Abstract: For the parameter estimation problem in research related to the fractional-order chaotic systems (FOCSs), a modified optimization algorithm based on Salp Swarm Algorithm (SSA) was developed in this paper. The proposed algorithm introduced several improvements on SSA: adding a grouping step, introducing “betrayal†behavior, and improving the update method of the followers. We applied multiple classical optimization algorithms to conduct the parameter estimation experiments on the fractional-order Lorenz chaotic system (Lorenz-FOCS) and the fractional-order Financial chaotic system (Financial-FOCS). In addition, we explored the impact of searching space on parameters estimation through experiments. The experimental results confirmed the feasibility of the modified Salp Swarm Algorithm (MSSA). The MSSA performed better than the SSA and other classical optimization algorithms in terms of the estimation accuracy and convergence rate.

Keywords: Parameter estimation; modified Salp Swarm Algorithm; fractional-order chaotic systems; swarm intelligence algorithm (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0129183123501310

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