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A Partition Based Bayesian Multi-objective Optimization Algorithm

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Numerical Computations: Theory and Algorithms (NUMTA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11974))

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Abstract

The research is aimed at coping with the inherent computational intensity of Bayesian multi-objective optimization algorithms. We propose the implementation which is based on the rectangular partition of the feasible region and circumvents much of computational burden typical for the traditional implementations of Bayesian algorithms. The included results of the solution of testing and practical problems illustrate the performance of the proposed algorithm.

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Acknowledgements

This work was supported by the Research Council of Lithuania under Grant No. P-MIP-17-61. We thank the reviewers for their valuable remarks.

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Authors and Affiliations

  1. Institute of Data Science and Digital Technologies, Vilnius University, Akademijos Street 4, 08663, Vilnius, Lithuania

    Antanas Žilinskas & Linas Litvinas

Authors
  1. Antanas Žilinskas
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  2. Linas Litvinas
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Corresponding author

Correspondence to Antanas Žilinskas .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Yaroslav D. Sergeyev

  2. University of Calabria, Rende, Italy

    Dmitri E. Kvasov

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Žilinskas, A., Litvinas, L. (2020). A Partition Based Bayesian Multi-objective Optimization Algorithm. In: Sergeyev, Y., Kvasov, D. (eds) Numerical Computations: Theory and Algorithms. NUMTA 2019. Lecture Notes in Computer Science(), vol 11974. Springer, Cham. https://doi.org/10.1007/978-3-030-40616-5_50

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  • DOI: https://doi.org/10.1007/978-3-030-40616-5_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-40615-8

  • Online ISBN: 978-3-030-40616-5

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