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A Partition Based Bayesian Multi-objective Optimization Algorithm

  • Antanas ŽilinskasEmail author
  • Linas Litvinas
Conference paper
  • 50 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11974)

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.

Keywords

Non-convex optimization Multi-objective optimization Bayesian approach 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Data Science and Digital TechnologiesVilnius UniversityVilniusLithuania

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