A Global Multi-Objective Bayesian Optimization Framework for Generic Machine Design Using Gaussian Process Regression

During the design of electrical machines, multiple performance objectives need to considered. Although stochastic optimization algorithms are extensively employed for this purpose, a primary drawback is the time-consuming and substantial number of design evaluations. Bayesian optimization (BO) presents an alternative that can address multi-objective optimization in particular for objective functions which are expensive to evaluate. Probabilistic surrogate models based on Gaussian process regression (GPR) form its basis. The high accuracy of Gaussian processes and their uncertainty estimation render Bayesian optimization extremely efficient and effective. Nevertheless, Bayesian optimization is hardly used in the field of electric machine design. Consequently, this study explores, analyses and evaluates the application of global multi-objective Bayesian optimization for machine design. Employing four distinct Bayesian acquisition functions, the study conducts a three-objective design optimization of a reluctance synchronous machine characterized by 14 design variables. A comprehensive comparison with the most common NSGA-II reveals that significantly superior outcomes are achievable within a considerably shorter time.