Abstract
This paper presents evolution strategy (ES) assisted by Kriging metamodel to preselect the most promising solutions and reduce the number of fitness function evaluations. The role of Kriging metamodel is to predict an objective function value for a new candidate solution by exploiting information recorded during previous generations. The use of promising solutions for screening the candidate solutions makes it possible to significantly reduce the computational cost of ES. The usefulness of the proposed method is verified by means of mathematical test cases and the optimal rotor structure design of interior permanent magnet synchronous machine.
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