Abstract
Real-world optimization problems often require the consideration of multiple contradicting objectives. These multiobjective problems are even more challenging when facing a limited budget of evaluations due to expensive experiments or simulations. In these cases, a specific class of multiobjective optimization algorithms (MOOA) has to be applied. This paper provides a review of contemporary multiobjective approaches based on the singleobjective meta-model-assisted ’Efficient Global Optimization’ (EGO) procedure and describes their main concepts. Additionally, a new EGO-based MOOA is introduced, which utilizes the \(\mathcal{S}\)-metric or hypervolume contribution to decide which solution is evaluated next. A benchmark on recently proposed test functions is performed allowing a budget of 130 evaluations. The results point out that the maximization of the hypervolume contribution within a real multiobjective optimization is superior to straightforward adaptations of EGO making our new approach capable of approximating the Pareto front of common problems within the allowed budget of evaluations.
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Ponweiser, W., Wagner, T., Biermann, D., Vincze, M. (2008). Multiobjective Optimization on a Limited Budget of Evaluations Using Model-Assisted \(\mathcal{S}\)-Metric Selection. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_78
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DOI: https://doi.org/10.1007/978-3-540-87700-4_78
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