Abstract
Within the last 10 years, many model-based multi-objective optimization algorithms have been proposed. In this paper, a taxonomy of these algorithms is derived. It is shown which contributions were made to which phase of the MBMO process. A special attention is given to the proposal of a set of points for parallel evaluation within a batch. Proposals for four different MBMO algorithms are presented and compared to their sequential variants within a comprehensive benchmark. In particular for the classic ParEGO algorithm, significant improvements are obtained. The implementations of all algorithm variants are organized according to the taxonomy and are shared in the open-source R package mlrMBO.
We acknowledge partial support by the Mercator Research Center Ruhr under grant Pr-2013-0015 Support-Vektor-Maschinen für extrem große Datenmengen, by the German Research Foundation (DFG) within the Collaborative Research Center SFB 823 Statistical modelling of nonlinear dynamic processes, project C2, and within the Collaborative Research Center SFB 708 3D-Surface Engineering, project C4. In addition, the authors acknowledge support by the French national research agency (ANR) within the Modèles Numérique project NumBBO – Analysis, Improvement and Evaluation of Numerical Blackbox Optimizers.
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Horn, D., Wagner, T., Biermann, D., Weihs, C., Bischl, B. (2015). Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9018. Springer, Cham. https://doi.org/10.1007/978-3-319-15934-8_5
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