This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

This paper investigates the efficient robust design and optimization of a high-temperature superconducting (HTS) linear synchronous motor by using the Taguchi parameter design approach. The manufacturing tolerances of the HTS magnets, primary iron core and the air gap are considered in the robust design to ensure that the optimal design is less sensitive to these uncertainties. To overcome the disadvantages of the conventional Taguchi parameter design approach, a sequential Taguchi robust optimization method is presented for improvement of the motor performance and manufacturing quality. The proposed method is efficient because it holds the advantages of both Taguchi method and sequential optimization strategy. It can significantly increase the average thrust and decrease the thrust ripple of the in-vestigated HTS linear synchronous motor.