Two-Level Surrogate-Assisted Differential Evolution Multi-Objective Optimization of Electric Machines Using 3-D FEA

Narges Taran, Dan M. Ionel, David G. Dorrell

Research output: Contribution to journalArticlepeer-review

66 Scopus citations

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.

Original languageEnglish
Article number8438503
JournalIEEE Transactions on Magnetics
Volume54
Issue number11
DOIs
StatePublished - Nov 2018

Bibliographical note

Publisher Copyright:
© 1965-2012 IEEE.

Keywords

  • 3-D finite-element analysis (FEA)
  • axial flux machines
  • kriging
  • optimization
  • surrogate model

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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