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Bin Xia,Ren Liu,Zhiwei He,Chang Seop Koh 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.1
A computationally effi cient surrogate model is suggested to approximate the objective and constraint function values, which replace expensive evaluation of the objective and constraint function values in numerical simulation-based optimization. Kriging surrogate model has been widely used in surrogate-based design optimization (SBDO) to replace the highly nonlinear black-box functions. In this paper, a novel adaptive Kriging model based on parallel infi lling strategy is proposed to improve both the numerical accuracy and effi ciency of the SBDO methods. The parallel infi lling strategy consists of two parts: local sampling and globaluthor sampling. In the local sampling, new additional sampling points are generated only within a limited region that is determined according to the optimal point at the last iteration, while in global sampling they are generated based on the fi tting error estimation in the whole region. The eff ectiveness of the proposed algorithm is verifi ed through applications to analytical functions. Then the algorithm is applied to the multi-objective optimal design of an ironless permanent magnet synchronous linear motor.