A New Robust Surrogate-Assisted Multi-Objective Optimization Algorithm for an IPMSM Design

For a multi-objective optimization problem applied to the electric machine design, a new robust surrogate-assisted algorithm is proposed in this research. The proposed algorithm can find a robust and well-distributed Pareto front set rapidly and precisely for robust nondominated solutions by using a kriging surrogate model and an uncertainty consideration with worst case scenario. The outstanding performances of the proposed algorithm are verified by a test function. Furthermore, through the application of the optimal design process of the interior permanent magnet synchronous motor, the feasibility of this algorithm is verified.