The performance of magnetic bearing is determined by its electromagnetic parameters and mechanical parameters. In order to improve the performance of hybrid magnetic bearing (HMB) to better meet the engineering requirements, which needs to be optimized, a multi-objective optimization method based on genetic particle swarm optimization algorithm (GAPSO) is proposed in this paper to solve the problem that the optimization objectives are not coordinated during the optimization design. By introducing the working principle of HMB, a mathematical model of suspension force is established, and its rationality is verified by the finite-element method. By optimization, the suspension force of the HMB is increased by 18.5%, and the volume is reduced by 22%. The optimization results show that the multi-objective optimization algorithm based on GAPSO can effectively improve the performance of HMB.

Optimization design is a satisfactory way to improve the performance of magnetic bearing (MB). In this paper, a multi-objective genetic algorithm of particle 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.