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Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers

1
State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
2
School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
3
School of Water Conservancy and Hydropower, Hebei University of Engineering, Handan 056038, China
*
Author to whom correspondence should be addressed.
Academic Editor: Mario Versaci
Mathematics 2021, 9(18), 2230; https://doi.org/10.3390/math9182230
Received: 13 August 2021 / Revised: 4 September 2021 / Accepted: 6 September 2021 / Published: 10 September 2021
(This article belongs to the Section Engineering Mathematics)
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. View Full-Text
Keywords: manta ray foraging optimization; magnetorheological damper; parameter identification; wavelet mutation; Levy flight; optimization algorithm manta ray foraging optimization; magnetorheological damper; parameter identification; wavelet mutation; Levy flight; optimization algorithm
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MDPI and ACS Style

Liao, Y.; Zhao, W.; Wang, L. Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers. Mathematics 2021, 9, 2230. https://doi.org/10.3390/math9182230

AMA Style

Liao Y, Zhao W, Wang L. Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers. Mathematics. 2021; 9(18):2230. https://doi.org/10.3390/math9182230

Chicago/Turabian Style

Liao, Yingying, Weiguo Zhao, and Liying Wang. 2021. "Improved Manta Ray Foraging Optimization for Parameters Identification of Magnetorheological Dampers" Mathematics 9, no. 18: 2230. https://doi.org/10.3390/math9182230

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