Multi-Objective Optimal Design of Bearingless Switched Reluctance Motor Based on Multi-Objective Genetic Particle Swarm Optimizer

In recent decades, bearingless switched reluctance motors (BSRMs) have been proposed. However, few researchers focused on the optimal design of the BSRMs. In this paper, the multi-objective optimal design of BSRMs is investigated. At first, an analytical design model is derived from the mathematical model of the BSRMs. An initial design is calculated by the analytical design model. The electromagnetic performance is compared with calculation results from the finite-element method (FEM). Then, the objective functions, constraints, and decision variables are also determined. Corresponding sensitivity analysis of the decision variables is implemented. Besides, aiming at solving the optimization problem with disconnected, non-uniformly distributed Pareto front and multiple local optimums, a novel multi-objective genetic particle swarm optimizer (MOGPSO) is presented. The algorithm performance of the proposed MOGPSO is validated by solving the standard test functions. Then the proposed MOGPSO is applied for the optimal design of BSRMs. Optimization results solved by MOGPSO, conventional multi-objective particle swarm optimizer, and non-dominated sorting genetic algorithm II are compared and analyzed. Comparison results reveal that the proposed MOGPSO can achieve more non-dominated solutions in Pareto front and is particularly suitable for optimization of BSRMs. The final optimal design is selected from the obtained Pareto front. The electromagnetic performance is compared with the initial design and verified by the FEM. Verification results show that the optimal design of BSRMs based on the analytical design model and the proposed MOGPSO is feasible and effective.