Abstract

A two-level surrogate-assisted optimization algorithm is proposed for electric machine design using 3-D finite-element analysis (FEA). The algorithm achieves the optima with much fewer FEA evaluations than conventional methods. It is composed of interior and exterior levels. The exploration is performed mainly in the interior level, which evaluates hundreds of designs employing affordable kriging models. Then, the most promising designs are evaluated in the exterior loop with expensive 3-D FEA models. The sample pool is constructed in a self-adjustable and dynamic way. A hybrid stopping criterion is used to avoid unnecessary expensive function evaluations.

Document Type

Article

Publication Date

11-2018

Notes/Citation Information

Published in IEEE Transactions on Magnetics, v. 54, issue, 11.

© 2018 IEEE Copyright Notice. “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.”

The document available for download is the authors’ manuscript version that is accepted for publication. The final published version is copyrighted by IEEE and will be available as: N. Taran, D. M. Ionel and D. G. Dorrell, “Two-Level Surrogate-Assisted Differential Evolution Multi-Objective Optmization of Electric Machines Using 3D FEA,” IEEE Transactions on Magnetics, Vol. 54, 2018, 5p. doi:10.1109/TMAG.2018.2856858

Digital Object Identifier (DOI)

https://doi.org/10.1109/TMAG.2018.2856858

Funding Information

The support of University of Kentucky, the L. Stanley Pigman endowment and the SPARK program, and of ANSYS Inc. is gratefully acknowledged.

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