volume | PIER Journals

Abstract

Optimization design is a satisfactory way to improve the performance of magnetic bearing (MB). In this paper, a multi-objective genetic particle algorithm of swarm optimization (GAPSO) is proposed for homopolar permanent magnet biased magnetic bearings (HPRMBs). By assigning different inertia weights to each objective function, the multi-objective function is transformed into a new single objective function for optimization. In order to ensure the diversity of particles in the optimization process, genetic algorithm is used to cross-mutate them, which enhances the global search ability of particle swarm optimization. After optimization with GAPSO, the levitating force of the MB is increased by 22.3%, the volume decreased by 26.6%, and the loss reduced by 33.9%. The optimization results show that the multi-objective optimization based on GAPSO can effectively improve the performance of HPRMB.

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Dr. Shengjing Yin

Dr. Fengxiao Huang

Dr. Yukun Sun

Professor Ye Yuan

Professor Yonghong Huang

Dr. Chi Chen