EAL, Institut für elektrische Antriebe und Leistungselektronik

Institut für elektrische Antriebe und Leistungselektronik

JKU, Johannes Kepler Universität Linz

Sprache: DE

Publikations Einzelansicht

A Hybrid Soft Computing Approach for Optimizing Design Parameters of Electrical Drives


Autor(en): Alexandru-Ciprian Zavoianu, Gerd Bramerdorfer, Edwin Lughofer, Siegfried Silber, Wolfgang Amrhein und Erich Peter Klement
Journal: SOCO 2012 Conference (Soft Computing Models in Industrial and Environmental Applications), ISBN 978-3-642-32921-0
Jahr: 2013
Band: 188
Seite(n): 347-358
Zusammenfassung: In this paper, we are applying a hybrid soft computing ap- proach for optimizing the performance of electrical drives where many degrees of freedom are allowed in the variation of design parameters. The hybrid nature of our approach originates from the application of multi-objective evolutionary algorithms (MOEAs) to solve the complex optimization problems combined with the integration of non-linear map- pings between design and target parameters. These mappings are based on artificial neural networks (ANNs) and they are used for the fitness evaluation of individuals (design parameter vectors). The mappings sub- stitute very time-intensive finite element simulations during a large part of the optimization run. Empirical results show that this approach finally reduces the computation time for single runs from a few days to several hours while achieving Pareto fronts with a similar high quality.

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