open access

https://hdl.handle.net/2115/87016

Fast Multi-Objective Optimization of Electromagnetic Devices Using Adaptive Neural Network Surrogate Model

英語

shape optimization

2022-05

IEEE (Institute of Electrical and Electronics Engineers)

IEEE transactions on magnetics

58(5)

8202209

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.

学術雑誌論文

Accepted Manuscript

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0018-9464

1941-0069

AA00667933