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Fast Multi-Objective Optimization of Electromagnetic Devices Using Adaptive Neural Network Surrogate Model
| Title: | Fast Multi-Objective Optimization of Electromagnetic Devices Using Adaptive Neural Network Surrogate Model |
| Authors: | Sato, Hayaho Browse this author | | Igarashi, Hajime Browse this author →KAKEN DB |
| Keywords: | Optimization | | Adaptation models | | Computational modeling | | Finite element analysis | | Artificial neural networks | | Statistics | | Sociology | | Genetic algorithm (GA) | | internal permanent magnet (IPM) motor | | machine learning | | magnetic shield | | shape optimization |
| Issue Date: | May-2022 |
| Publisher: | IEEE (Institute of Electrical and Electronics Engineers) |
| Journal Title: | IEEE transactions on magnetics |
| Volume: | 58 |
| Issue: | 5 |
| Start Page: | 8202209 |
| Publisher DOI: | 10.1109/TMAG.2022.3150271 |
| Abstract: | This article presents a fast population-based multi-objective optimization of electromagnetic devices using an adaptive neural network (NN) surrogate model. The proposed method does not require any training data or construction of a surrogate model before the optimization phase. Instead, the NN surrogate model is built from the initial population in the optimization process, and then it is sequentially updated with high-ranking individuals. All individuals were evaluated using the surrogate model. Based on this evaluation, high-ranking individuals are reevaluated using high-fidelity electromagnetic field computation. The suppression of the execution of expensive field computations effectively reduces the computing costs. It is shown that the proposed method works two to four times faster, maintaining optimization performance than the original method that does not use surrogate models. |
| Rights: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/87016 |
| 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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