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

M{\'e}thodologie de dimensionnement par optimisation bay{\'e}sienne d'une machine synchro-r{\'e}luctante assist{\'e}e d'aimants permanents