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Hokkaido University Collection of Scholarly and Academic Papers >
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Peer-reviewed Journal Articles, etc >
Multi-objective optimization of permanent magnet motors using deep learning and CMA-ES
| Title: | Multi-objective optimization of permanent magnet motors using deep learning and CMA-ES |
| Authors: | Mikami, Ryosuke Browse this author | | Sato, Hayaho Browse this author | | Hayashi, Shogo Browse this author | | Igarashi, Hajime Browse this author |
| Keywords: | Deep learning | | CNN | | multi-objective optimization | | CMA-ES | | NSGA-II | | PM motor |
| Issue Date: | 14-Dec-2023 |
| Publisher: | IOS Press |
| Journal Title: | International journal of applied electromagnetics and mechanics |
| Volume: | 73 |
| Issue: | 4 |
| Start Page: | 255 |
| End Page: | 264 |
| Publisher DOI: | 10.3233/JAE-230077 |
| Abstract: | 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. |
| Rights: | The final publication is available at IOS Press through http://dx.doi.org/10.3233/JAE-230077 |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/91227 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 五十嵐 一
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