This paper aims to propose a numerically efficient multi-objective optimization strategy, which can improve both the efficiency and performance during the optimization process.
This paper discusses the multi-objective optimization algorithm by combining multi-objective differential evolution (MODE) algorithm with an adaptive dynamic Taylor Kriging (ADTK) model.
The proposed approach is validated through application to an analytic example and applied to a shape optimal design of a multi-layered interior permanent magnet synchronous motor for torque ripple reduction while maintaining the average torque.
The ADTK model selects its basis functions adaptively and dynamically so that it may have better accuracy than any other Kriging models. Through adaptive insertion of new sampling data, it guarantees minimum required sampling data for a desired fitting accuracy.
This work was supported by the Basic Science Research Program through the Ministry of Education, National Research Foundation of Korea, under Grant 2014R1A1A4A03004537, and in part by the Doctoral Scientific Research Foundation of Liaoning Province of China, under Grant 20170520219.
Xia, B., Yeon, J. and Koh, C.S. (2018), "Optimal shape design of multi-layered IPMSM using adaptive dynamic Taylor Kriging model", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 37 No. 2, pp. 581-590. https://doi.org/10.1108/COMPEL-12-2016-0527Download as .RIS
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