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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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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