Abstract
In this paper, we are applying a hybrid soft computing approach 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 mappings 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 substitute 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.
This work was conducted in the realm of the research program at the Austrian Center of Competence in Mechatronics (ACCM), which is a part of the COMET K2 program of the Austrian government. The work-related projects are kindly supported by the Austrian government, the Upper Austrian government and the Johannes Kepler University Linz. The authors thank all involved partners for their support. This publication reflects only the authors’ views.
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Zăvoianu, AC., Bramerdorfer, G., Lughofer, E., Silber, S., Amrhein, W., Klement, E.P. (2013). A Hybrid Soft Computing Approach for Optimizing Design Parameters of Electrical Drives. In: Snášel, V., Abraham, A., Corchado, E. (eds) Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32922-7_36
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DOI: https://doi.org/10.1007/978-3-642-32922-7_36
Publisher Name: Springer, Berlin, Heidelberg
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