Solving hierarchical optimization problems using MOEAs

Authors:
Christian Haubelt;Sanaz Mostaghim;Jürgen Teich;Ambrish Tyagi
Affiliations:
Department of Computer Science, Hardware-Software-Co-Design, University of Erlangen-Nuremberg;Computer Engineering Laboratory, Department of Electrical Engineering and Information Technology, University of Paderborn;Department of Computer Science, Hardware-Software-Co-Design, University of Erlangen-Nuremberg;Computer Engineering Laboratory, Department of Electrical Engineering and Information Technology, University of Paderborn
Venue:
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Year:
2003

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Abstract

In this paper, we propose an approach for solving hierarchical multi-objective optimization problems (MOPs). In realistic MOPs, two main challenges have to be considered: (i) the complexity of the search space and (ii) the non-monotonicity of the objective-space. Here, we introduce a hierarchical problem description (chromosomes) to deal with the complexity of the search space. Since Evolutionary Algorithms have been proven to provide good solutions in non-monotonic objective-spaces, we apply genetic operators also on the structure of hierarchical chromosomes. This novel approach decreases exploration time substantially. The example of system synthesis is used as a case study to illustrate the necessity and the benefits of hierarchical optimization.