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Adaptive Hyperparameter Fine-Tuning for Boosting the Robustness and Quality of the Particle Swarm Optimization Algorithm for Non-Linear RBF Neural Network Modelling and Its Applications

Author

Listed:
  • Zohaib Ahmad

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100024, China)

  • Jianqiang Li

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100024, China
    Beijing Engineering Research Center for IoT Software and Systems, Beijing 100124, China)

  • Tariq Mahmood

    (Faculty of Information Sciences, Vehari Campus, University of Education, Vehari 61100, Pakistan)

Abstract

A method is proposed for recognizing and predicting non-linear systems employing a radial basis function neural network (RBFNN) and robust hybrid particle swarm optimization (HPSO) approach. A PSO is coupled with a spiral-shaped mechanism (HPSO-SSM) to optimize the PSO performance by mitigating its constraints, such as sluggish convergence and the local minimum dilemma. Three advancements are incorporated into the hypothesized HPSO-SSM algorithms to achieve remarkable results. First, the diversity of the search process is promoted to update the inertial weight ω based on the logistic map sequence. Then, two distinct parameters are trained in the original position update algorithm to enhance the work efficiency of the successive generation. Finally, the proposed approach employs a spiral-shaped mechanism as a local search operator inside the optimum solution space. Moreover, the HPSO-SSM method concurrently improves the RBFNN parameters and network size, building a model with a compact configuration and higher precision. Two non-linear benchmark functions and the total phosphorus (TP) modelling issue in a waste water treatment process (WWTP) are utilized to assess the overall efficacy of the creative technique. The results of testing indicate that the projected HPSO-SSM-RBFNN algorithm performed very effectively.

Suggested Citation

  • Zohaib Ahmad & Jianqiang Li & Tariq Mahmood, 2023. "Adaptive Hyperparameter Fine-Tuning for Boosting the Robustness and Quality of the Particle Swarm Optimization Algorithm for Non-Linear RBF Neural Network Modelling and Its Applications," Mathematics, MDPI, vol. 11(1), pages 1-16, January.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:1:p:242-:d:1023463
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    Citations

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    Cited by:

    1. Van-Hien Nguyen & Tri Cuong Do & Kyoung-Kwan Ahn, 2024. "Implementing PSO-LSTM-GRU Hybrid Neural Networks for Enhanced Control and Energy Efficiency of Excavator Cylinder Displacement," Mathematics, MDPI, vol. 12(20), pages 1-21, October.
    2. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2023. "Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation," Energy, Elsevier, vol. 285(C).

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