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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Qingwen Cai & Renhuan Yang & Chao Shen & Kelong Yue & Yibin Chen, 2023. "A modified Salp Swarm Algorithm for parameter estimation of fractional-order chaotic systems," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(10), pages 1-15, October.
    • Handle: RePEc:wsi:ijmpcx:v:34:y:2023:i:10:n:s0129183123501310
    DOI: 10.1142/S0129183123501310
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