Multi-objective optimization of permanent magnet motors using deep learning and CMA-ES

Mikami, Ryosuke; Sato, Hayaho; Hayashi, Shogo et al.

International journal of applied electromagnetics and mechanics, 2023, 73(4), 255-264

Permalink : https://hdl.handle.net/2115/91227

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アクセス権
open access
URI
https://hdl.handle.net/2115/91227
タイトル
Multi-objective optimization of permanent magnet motors using deep learning and CMA-ES
著者
Mikami, Ryosuke ; Sato, Hayaho ; Hayashi, Shogo ; Igarashi, Hajime
著者名
Mikami, Ryosuke
著者名
Sato, Hayaho
著者名
Hayashi, Shogo
著者名
Igarashi, Hajime
言語
英語
キーワード
Deep learning
CNN
multi-objective optimization
CMA-ES
NSGA-II
PM motor
発行日
2023-12-14
出版者
IOS Press
収録物名
International journal of applied electromagnetics and mechanics
巻(号)
73(4)
ページ
255-264
抄録
This paper proposes a multi-objective optimization method for permanent magnet motors using a fast optimization algorithm, Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and deep learning. Multi-objective optimization with topology optimization is effective in the design of permanent magnet motors. Although CMA-ES needs fewer population size than genetic algorithm for single objective problems, this is not evident for multi-objective problems. For this reason, the proposed method generates training data by solving the single-objective optimization multiple times using CMA-ES, and constructs a deep neural network (NN) based on the data to predict performance from motor images at high speed. The deep NN is then used for fast solution of multi-objective optimization problems. Numerical examples demonstrate the effectiveness of the proposed method.
資源タイプ
学術雑誌論文
出版タイプ
Accepted Manuscript
権利情報
The final publication is available at IOS Press through http://dx.doi.org/10.3233/JAE-230077
PISSN
1383-5416
関連情報 (isVersionOf)
出版社版DOI
https://doi.org/10.3233/JAE-230077