A New Robust Surrogate-Assisted Multi-Objective Optimization Algorithm for an IPMSM Design

被引：0

作者：

Lim, Dong-Kuk ^{[1
]}

Woo, Dong-Kyun ^{[2
]}

Yeo, Han-Kyeol ^{[1
]}

Jung, Sang-Yong ^{[3
]}

Jung, Hyun-Kyo ^{[1
]}

机构：

[1] Seoul Natl Univ, Dept Elect & Comp Engn, Seoul 151742, South Korea

[2] Yeungnam Univ, Dept Elect Engn, Gyeongbuk 712749, South Korea

[3] Sungkyunkwan Univ, Sch Elect & Elect Engn, Suwon 440746, South Korea

来源：

2016 IEEE CONFERENCE ON ELECTROMAGNETIC FIELD COMPUTATION (CEFC) | 2016年

关键词：

Interior permanent magnet synchronous motor; multi-objective; robust optimization; surrogate model;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

For a multi-objective optimization problem applied to the electric machine design, a new robust surrogate-assisted algorithm is proposed in this research. The proposed algorithm can find a robust and well-distributed Pareto front set rapidly and precisely for robust nondominated solutions by using a kriging surrogate model and an uncertainty consideration with worst case scenario. The outstanding performances of the proposed algorithm are verified by a test function. Furthermore, through the application of the optimal design process of the interior permanent magnet synchronous motor, the feasibility of this algorithm is verified.

引用

页数：1

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