Abstract
This paper presents an optimization framework for the robust design of electrical machines. During the run of a genetic algorithm, all evaluated solutions are kept in an archive, and are used to build different meta-models for the assessment of robustness. The proposed robust design algorithms are verified using analytical test functions and are shown to be efficient and accurate. They have also been applied to the robust design of a surface mount permanent magnet machine considering manufacturing imprecision and different optimal designs (robust and non-robust) are obtained.
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