  In this article, three optimization approaches are exploited to improve the
performance of a permanent magnet-assisted synchronous reluctance machine: a
first optimization using fixed substitution models and two Bayesian
optimization approaches based on adaptive substitution models. The results show
that Bayesian approaches lead to machines with better performance using the
same computation time (same number of finite element simulations). Unlike
optimization methodologies based on fixed substitution models, Bayesian
approaches provide solutions directly based on finite element simulations and,
therefore, do not require verification.
