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

Abstract

This paper presents a fast population-based multi-objective optimization of electromagnetic devices using an adaptive neural network surrogate model. The proposed method does not require any training data or construction of a surrogate model before the optimization phase. Instead, the neural network 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 re-evaluated 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.

Ultra‐fast finite element analysis of coreless axial flux permanent magnet synchronous machines

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Synergistic Optimization Design Method to Improve the Overload Capacity and Efficiency of Canned Permanent Magnet Synchronous Motor for Vacuum Pump

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