Review of surrogate model assisted multi-objective design optimization of electrical machines: new opportunities and challenges

This paper overviews surrogate model-assisted multi-objective design optimization techniques of
electrical machines for efficient, accurate, and robust design optimization to ease design issues due
to unprecedentedly increasing machine performance requirements. Firstly, the mechanism of
surrogate-assisted modeling is introduced by comparing it with conventional physical modeling
approaches. The relevant techniques are then categorized and subsequently reviewed in terms of
the design of experiments, surrogate model construction, and multi-objective optimization
algorithms. The potential application prospects for machine design optimization are highlighted.
Finally, three surrogate-assisted modeling methods, i.e., transfer learning-based models, gradient
sampling-based multi-fidelity models, and search space decay-based surrogate models, are
quantitively compared by applying them to the design optimization of a five-phase permanent
magnet synchronous machine.