Development of Differing Extent Mesh Adaptive Direct Search Applied for Optimal Design of Spoke-Type PMSM

Optimal design of electric machines using finite-element analysis (FEA) necessitates too much computation time to maintain high accuracy. To reduce the computation time and obtain a global optimum in multi-modal problems, this paper presents differing extent mesh adaptive direct search (DEMADS). The proposed algorithm is classified into three groups according to the poll searching frame and mesh size for assigning a global and local searching. In particular, DEMADS shares information among solutions based on a parallel multi-start strategy for earlier stopping by deviation and checking revisits. The effectiveness of DEMADS has been validated and evaluated as the function call number and the convergence number at a global optimum using benchmarking function. Finally, it is applied to an optimization problem that minimize torque ripple of a spoke-type permanent magnet synchronous machine via FEA.