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Adaptive Dimensionality Reduction in Multiobjective Optimization with Multiextremal Criteria

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Machine Learning, Optimization, and Data Science (LOD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11331))

Abstract

The paper is devoted to consideration of multicriterial optimization (MCO) problems subject to multiextremality of criteria. Application of convolution techniques for finding partial Pareto-optimal solutions generates under this assumption the multiextremal problems of scalar optimization. For solving these problems it is necessary to use efficient global optimization algorithms. As such the methods the nested schemes of dimensionality reduction in combination with univariate characteristical optimization algorithms are considered. A general description of the scheme is given and its modification accelerating the search is presented. Efficiency of the proposed approach is demonstrated on the base of representative computational experiment on a test class of bi-criterial MCO problems with essentially multiextremal criteria.

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Acknowledgements

The research has been supported by the Russian Science Foundation, project No 16-11-10150 “Novel efficient methods and software tools for time-consuming decision make problems using superior-performance supercomputers.”

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Authors and Affiliations

  1. Lobachevsky State University, Gagarin Avenue 23, 603950, Nizhni Novgorod, Russia

    Victor Gergel, Vladimir Grishagin & Ruslan Israfilov

Authors
  1. Victor Gergel
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  2. Vladimir Grishagin
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  3. Ruslan Israfilov
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Corresponding author

Correspondence to Vladimir Grishagin .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy and University of Reading, Reading, UK

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

  5. IBM, Tivoli Research Lab, Rome, Italy

    Vincenzo Sciacca

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Gergel, V., Grishagin, V., Israfilov, R. (2019). Adaptive Dimensionality Reduction in Multiobjective Optimization with Multiextremal Criteria. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_11

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  • DOI: https://doi.org/10.1007/978-3-030-13709-0_11

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-13709-0

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