MOI-MBO: Multiobjective Infill for Parallel Model-Based Optimization
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Abstract
The aim of this work is to compare different approaches for parallelization in model-based optimization. As another alternative aside from the existing methods, we propose using a multi-objective infill criterion that rewards both the diversity and the expected improvement of the proposed points. This criterion can be applied more universally than the existing ones because it has less requirements. Internally, an evolutionary algorithm is used to optimize this criterion. We verify the usefulness of the approach on a large set of established benchmark problems for black-box optimization. The experiments indicate that the new method’s performance is competitive with other batch techniques and single-step EGO.
Keywords
Multiobjective Optimization Kriging Model Multimodal Function Expect Improvement Local Uncertainty
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Notes
Acknowledgements
This paper is based on investigations of the projects B3 and C2 of the Collaborative Research Center SFB 823, which are kindly supported by Deutsche Forschungsgemeinschaft (DFG). It is also partly supported by the French national research agency (ANR) within the Modeles Numeriques project NumBBO. The authors also thank Tobias Wagner for fruitful discussions of multiobjective infill criteria.
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