Multicriteria Optimal Latin Hypercube Design-Based Surrogate-Assisted Design Optimization for a Permanent-Magnet Vernier Machine

This paper proposes an efficient surrogate-assisted design optimization method based on a multicriteria optimal Latin hypercube design (LHD) for multi-objective optimization of a surface-mounted permanent-magnet vernier machine (SPMVM). An improved iterated local search (ILS) method is proposed to optimize the spatial distribution uniformity and orthogonality of LHDs so that the data feature over the wide ranges of optimization variables can be captured more efficiently. Using the optimal LHD, a highly generalizable surrogate model can be trained with fewer samples, thus greatly reducing the required number of FEA cases and improving the optimization efficiency. A prototype corresponding to the optimal design is built and measured to validate the proposed method.