Particle Swarm Optimization with Multiple Regression for Optimal Design of Interior Permanent Magnet Synchronous Motor
Chan-Ho Kim, Jong-Wook Kim, Yong-Jae Kim, Sang‐Yong Jung · 2019
In this study, we propose to apply multiple regression method to improve the performance of the conventional particle swarm optimization (PSO) algorithm. We can estimate the function formula for the design variables and derive the global solution by using estimated function formula. The convergence speed of the conventional PSO algorithm is improved by using the information of the solution obtained by performing the multiple regression. The proposed algorithm is validated using numerical test function. After the algorithm is validated, it is applied to the optimal design of an interior permanent magnet synchronous motor (IPMSM), aiming at minimizing the torque ripple and cogging torque. The torque characteristic data of IPMSM required for performing the optimization are obtained using finite element method based on 2-D numerical analysis.