Machine-Learning Aided Multiobjective Optimization of Electric Machines— Geometric-Feasibility and Enhanced Regression Models

This article deals with the optimization of electrical machines by means of artificial neural network (ANN)-based classification and regression models. Geometrically (or otherwise) unfeasible designs are detected with high accuracy during the optimization process by means of an ad hoc classification model, whereas continuous targets are predicted through regression models. Training samples are generated with an expensive finite-element (FE) model, resulting in small training sets. Moreover, the design optimization normally involves multiple but correlated subobjectives. The correlation can be leveraged using chained regression or a multioutput ANN; it is shown that both methods can achieve higher predictive performance than predicting the targets separately. The developed methods are successfully applied to a permanent-magnet synchronous machine (PMSM) with 12 geometric parameters and four subobjectives, and considering both no-load and load operation. The results show very good predictive performance of the ANN models and a significant reduction of the computational effort. The optimization can thus be run several times, with, e.g., modified weighting of the subobjectives, with little extra cost.