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Branch and probability bound methods in multi-objective optimization

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Abstract

An approach to non-convex multi-objective optimization problems is considered where only the values of objective functions are required by the algorithm. The proposed approach is a generalization of the probabilistic branch-and-bound approach well applicable to complicated problems of single-objective global optimization. In the present paper the concept of probabilistic branch-and-bound based multi-objective optimization algorithms is discussed, and some illustrations are presented.

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Acknowledgments

This research has been done under support of The Lithuanian Research Council, Grant SF3-92/TYR-064.

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Authors and Affiliations

  1. Institute of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania

    Antanas Z̆ilinskas

  2. Department of Mathematics, Cardiff University, Cardiff, UK

    Anatoly Zhigljavsky

Authors
  1. Antanas Z̆ilinskas
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  2. Anatoly Zhigljavsky
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Correspondence to Antanas Z̆ilinskas.

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Z̆ilinskas, A., Zhigljavsky, A. Branch and probability bound methods in multi-objective optimization. Optim Lett 10, 341–353 (2016). https://doi.org/10.1007/s11590-014-0777-z

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  • DOI: https://doi.org/10.1007/s11590-014-0777-z

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