October 7, 2013

Paper: Hybridization of Multi-Objective Evolutionary Algorithms and Artificial Neural Networks for Optimizing the Performance of Electrical Drives - EAAI

A big part of my current PhD work at the JKU-Department of Knowledge-Based Mathematical Systems is related to the dissemination of our current research (i.e., writing scientific articles). That's why most of my recent / forthcoming posts are / will be about "papers".

The work presented in this post is a revised and extended (journal) version of one of the earlier papers written in collaboration with our partners from the Institute for Electrical Drives and Power Electronics of the Johannes Kepler University, Linz. The aim of the article is to describe a surrogate-based enhancement that can help to significantly speed-up a multi-objective evolutionary algorithm that requires an extremely time-intensive fitness evaluation function. Here is the abstract of the article:
Performance optimization of electrical drives implies a lot of degrees of freedom in the variation of design parameters, which in turn makes the process overly complex and sometimes impossible to handle for classical analytical optimization approaches. This, and the fact that multiple non-independent design parameter have to be optimized synchronously, makes a soft computing approach based on multi-objective evolutionary algorithms (MOEAs) a feasible alternative. In this paper, we describe the application of the well known Non-dominated Sorting Genetic Algorithm II (NSGA-II) in order to obtain high-quality Pareto-optimal solutions for three optimization scenarios. The nature of these scenarios requires the usage of fitness evaluation functions that rely on very time-intensive finite element (FE) simulations. The key and novel aspect of our optimization procedure is the on-the-fly automated creation of highly accurate and stable surrogate fitness functions based on artificial neural networks (ANNs). We employ these surrogate fitness functions in the middle and end parts of the NSGA-II run (=> hybridization) in order to significantly reduce the very high computational effort required by the optimization process. The results show that by using this hybrid optimization procedure, the computation time of a single optimization run can be reduced by 46% to 72% while achieving Pareto-optimal solution sets with similar, or even slightly better, quality as those obtained when conducting NSGA-II runs that use FE simulations over the whole run-time of the optimization process.
You can download the preprint version of the paper by clicking here or from my Downloads box (Hybridization of Multi-Objective Evolutionary Algorithms and Artificial Neural Networks for Optimizing the Performance of Electrical Drives - EAAI 2013.pdf). The same preprint version can be previewed at the bottom of this post. The original publication is available at elsevier.com.

For citations please use the following BibTeX reference:

  author = {Alexandru-Ciprian Z\u{a}voianu and Gerd Bramerdorfer and Edwin Lughofer and Siegfried Silber and Wolfgang Amrhein and Erich Peter Klement},
  title = {Hybridization of Multi-Objective Evolutionary Algorithms and Artificial Neural Networks for Optimizing the Performance of Electrical Drives},
  journal = {Engineering Applications of Artificial Intelligence},
  year = {2013},
  volume = {26},
  pages = {1781-1794},
  number = {8},
  doi = {10.1016/j.engappai.2013.06.002}

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