A multi-point mechanism of expected hypervolume improvement for parallel multi-objective Bayesian global optimization

Kaifeng Yang, Pramudita Satria Palar, Michael Emmerich, Koji Shimoyama, Thomas Bäck

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The technique of parallelization is a trend in the field of Bayesian global optimization (BGO) and is important for real-world applications because it can make full use of CPUs and speed up the execution times. This paper proposes a multi-point mechanism of the expected hypervolume improvement (EHVI) for multi-objective BGO (MOBGO) by the utilization of the truncated EHVI (TEHVI). The basic idea is to divide the objective space into several sub-objective spaces and then search for the optimal solutions in each sub-objective space by using the TEHVI as the infill criterion. We studied the performance of the proposed algorithm and performed comparisons with Kriging believer technique (KB) on five scientific benchmarks and a real-world application problem (i.e., a low-fidelity multi-objective airfoil optimization design). The stochastic experimental results show that the proposed algorithm performs better than the KB with respect to the hypervolume indicator, indicating that the proposed method provides an efficient parallelization technique for MOBGO.

Original languageEnglish
Title of host publicationGECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Pages656-663
Number of pages8
ISBN (Electronic)9781450361118
DOIs
Publication statusPublished - 2019 Jul 13
Event2019 Genetic and Evolutionary Computation Conference, GECCO 2019 - Prague, Czech Republic
Duration: 2019 Jul 132019 Jul 17

Publication series

NameGECCO 2019 - Proceedings of the 2019 Genetic and Evolutionary Computation Conference

Conference

Conference2019 Genetic and Evolutionary Computation Conference, GECCO 2019
CountryCzech Republic
CityPrague
Period19/7/1319/7/17

Keywords

  • Expected Hypervolume Improvement
  • Kriging
  • Multi-Objective Bayesian Global Optimization
  • Parallelization

ASJC Scopus subject areas

  • Computational Mathematics

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