A new robust surrogate-assisted multi-objective optimization algorithm for an IPMSM design

Dong Kuk Lim; Dong Kyun Woo; Han Kyeol Yeo; Sang Yong Jung; Hyun Kyo Jung

doi:10.1109/CEFC.2016.7815908

A new robust surrogate-assisted multi-objective optimization algorithm for an IPMSM design

Dong Kuk Lim
, Dong Kyun Woo
, Han Kyeol Yeo
, Sang Yong Jung
, Hyun Kyo Jung

Department of Electronic and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509010325
DOIs	https://doi.org/10.1109/CEFC.2016.7815908
State	Published - 12 Jan 2017
Event	17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016 - Miami, United States Duration: 13 Nov 2016 → 16 Nov 2016

Publication series

Name	IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation

Conference

Conference	17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016
Country/Territory	United States
City	Miami
Period	13/11/16 → 16/11/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Interior permanent magnet synchronous motor
Multi-objective
Robust optimization
Surrogate model

Access to Document

10.1109/CEFC.2016.7815908

Cite this

Lim, D. K., Woo, D. K., Yeo, H. K., Jung, S. Y., & Jung, H. K. (2017). A new robust surrogate-assisted multi-objective optimization algorithm for an IPMSM design. In IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation Article 7815908 (IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEFC.2016.7815908

Lim, Dong Kuk ; Woo, Dong Kyun ; Yeo, Han Kyeol et al. / A new robust surrogate-assisted multi-objective optimization algorithm for an IPMSM design. IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation. Institute of Electrical and Electronics Engineers Inc., 2017. (IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation).

@inproceedings{74491b024d604ed8af0e1eec4f244af1,

title = "A new robust surrogate-assisted multi-objective optimization algorithm for an IPMSM design",

abstract = "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.",

keywords = "Interior permanent magnet synchronous motor, Multi-objective, Robust optimization, Surrogate model",

author = "Lim, \{Dong Kuk\} and Woo, \{Dong Kyun\} and Yeo, \{Han Kyeol\} and Jung, \{Sang Yong\} and Jung, \{Hyun Kyo\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016 ; Conference date: 13-11-2016 Through 16-11-2016",

year = "2017",

month = jan,

day = "12",

doi = "10.1109/CEFC.2016.7815908",

language = "English",

series = "IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation",

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Lim, DK, Woo, DK, Yeo, HK, Jung, SY & Jung, HK 2017, A new robust surrogate-assisted multi-objective optimization algorithm for an IPMSM design. in IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation., 7815908, IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation, Institute of Electrical and Electronics Engineers Inc., 17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016, Miami, United States, 13/11/16. https://doi.org/10.1109/CEFC.2016.7815908

A new robust surrogate-assisted multi-objective optimization algorithm for an IPMSM design. / Lim, Dong Kuk; Woo, Dong Kyun; Yeo, Han Kyeol et al.
IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation. Institute of Electrical and Electronics Engineers Inc., 2017. 7815908 (IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A new robust surrogate-assisted multi-objective optimization algorithm for an IPMSM design

AU - Lim, Dong Kuk

AU - Woo, Dong Kyun

AU - Yeo, Han Kyeol

AU - Jung, Sang Yong

AU - Jung, Hyun Kyo

PY - 2017/1/12

Y1 - 2017/1/12

N2 - 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.

AB - 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.

KW - Interior permanent magnet synchronous motor

KW - Multi-objective

KW - Robust optimization

KW - Surrogate model

UR - https://www.scopus.com/pages/publications/85011999860

U2 - 10.1109/CEFC.2016.7815908

DO - 10.1109/CEFC.2016.7815908

M3 - Conference contribution

AN - SCOPUS:85011999860

T3 - IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation

BT - IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th Biennial IEEE Conference on Electromagnetic Field Computation, IEEE CEFC 2016

Y2 - 13 November 2016 through 16 November 2016

ER -

Lim DK, Woo DK, Yeo HK, Jung SY, Jung HK. A new robust surrogate-assisted multi-objective optimization algorithm for an IPMSM design. In IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation. Institute of Electrical and Electronics Engineers Inc. 2017. 7815908. (IEEE CEFC 2016 - 17th Biennial Conference on Electromagnetic Field Computation). doi: 10.1109/CEFC.2016.7815908

A new robust surrogate-assisted multi-objective optimization algorithm for an IPMSM design

Abstract

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UN SDGs

Keywords

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