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Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

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  • Yingying Liao

    (State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
    School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China)

  • Weiguo Zhao

    (School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan 056038, China)

  • Liying Wang

    (School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan 056038, China)

Abstract

Magnetorheological (MR) dampers play a crucial role in various engineering systems, and how to identify the control parameters of MR damper models without any prior knowledge has become a burning problem. In this study, to identify the control parameters of MR damper models more accurately, an improved manta ray foraging optimization (IMRFO) is proposed. The new algorithm designs a searching control factor according to a weak exploration ability of MRFO, which can effectively increase the global exploration of the algorithm. To prevent the premature convergence of the local optima, an adaptive weight coefficient based on the Levy flight is designed. Moreover, by introducing the Morlet wavelet mutation strategy to the algorithm, the mutation space is adaptively adjusted to enhance the ability of the algorithm to step out of stagnation and the convergence rate. The performance of the IMRFO is evaluated on two sets of benchmark functions and the results confirm the competitiveness of the proposed algorithm. Additionally, the IMRFO is applied in identifying the control parameters of MR dampers, the simulation results reveal the effectiveness and practicality of the IMRFO in the engineering applications.

Suggested Citation

  • Yingying Liao & Weiguo Zhao & Liying Wang, 2021. "Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers," Mathematics, MDPI, vol. 9(18), pages 1-38, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2230-:d:633314
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    References listed on IDEAS

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    3. Boreiry, Mahya & Ebrahimi-Nejad, Salman & Marzbanrad, Javad, 2019. "Sensitivity analysis of chaotic vibrations of a full vehicle model with magnetorheological damper," Chaos, Solitons & Fractals, Elsevier, vol. 127(C), pages 428-442.
    4. Mohammad Soleimani Amiri & Rizauddin Ramli & Mohd Faisal Ibrahim & Dzuraidah Abd Wahab & Norazam Aliman, 2020. "Adaptive Particle Swarm Optimization of PID Gain Tuning for Lower-Limb Human Exoskeleton in Virtual Environment," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
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