A multi-objective optimisation method of rail combination profile in high-speed turnout switch panel

Fang, Jiasheng; Chen, Rong; Chen, Jiayin; Xu, Jingmang; Wang, Ping

doi:10.1080/00423114.2022.2052327

Cited by 11 publications

(1 citation statement)

References 19 publications

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“…Nuaekaew et al 26 proposed a dual archival multi-objective Grey Wolf Optimization algorithm (2ArchMGWO) for solving multi-objective optimal power scheduling problems. For NSGA-II, Wang et al 27 designed CN-NSGA-II by adding chaotic mapping and improving the crossover method for solving the optimal contour of high-speed orbit, while Xu et al 28 proposed to combine MOPSO with NSGA-II for obtaining the optimal design parameters of aero-engine baffles. However, for the optimization problem of magnetic levitation control parameters, MOGWO is prone to fall into local optimum while NSGA-II has poor convergence performance, which leads to the inability of the two to meet the requirements of solving multi-objective problems.…”

Section: Introductionmentioning

confidence: 99%

Parameter optimization of electromagnetic suspension-type maglev train control system based on multi-objective grey wolf non-dominated sorting hybrid algorithm-Ⅱ hybrid algorithm

Wang,

Zeng,

Liu

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

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This paper presents a novel hybrid algorithm based on CMOGWO-ADNSGA-II to solve the vibration stability problem during the operation of a EMS-type maglev train dynamics model subjected to strong non-linear magnetic buoyancy. The proposed algorithm optimizes the control system parameters of EMS-type maglev train suspensions by combining an improved multi-objective chaotic grey wolf algorithm (CMOGWO) with an improved non-dominated Sorting genetic algorithm-II (ADNSGA-II) to enhance the search capability of the algorithm and ensure population diversity. The efficacy of the algorithm is demonstrated by applying it to the EMS-type maglev train suspension frame control system to find the optimal control parameters. Experimental results show that the system with the optimal parameters applied significantly reduces the suspension gap amplitude and the corresponding standard deviation, as well as the vertical acceleration amplitude and the corresponding standard deviation during operation. The proposed algorithm provides a good solution for EMS-type maglev train suspension vibration control, which can improve its performance and safety.

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Section: Introductionmentioning

confidence: 99%