A Hybrid Soft Computing Approach for Optimizing Design Parameters of Electrical Drives
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.
Keywordshybrid soft computing methods multi-objective genetic algorithms feed-forward artificial neural networks electrical drives
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