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.
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This work was supported in part by the German Science Foundation (DFG), SPP 1040.
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Haubelt, C., Mostaghim, S., Teich, J., Tyagi, A. (2003). Solving Hierarchical Optimization Problems Using MOEAs. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_12
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DOI: https://doi.org/10.1007/3-540-36970-8_12
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