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International Conference on Learning and Intelligent Optimization

LION 12 2018: Learning and Intelligent Optimization pp 78–81Cite as

A Global Optimization Algorithm for Non-Convex Mixed-Integer Problems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11353))

Abstract

In the present paper, the mixed-integer global optimization problems are considered. A novel deterministic algorithm for solving the problems of this class based on the information-statistical approach to solving the continuous global optimization problems has been proposed. The comparison of this algorithm with known analogs demonstrating the efficiency of the developed approach has been conducted. The stable operation of the algorithm was confirmed also by solving a series of several hundred mixed-integer global optimization problems.

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Acknowledgments

This study was supported by the Russian Science Foundation, project No 16-11-10150.

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Authors and Affiliations

  1. Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia

    Victor Gergel, Konstantin Barkalov & Ilya Lebedev

Authors
  1. Victor Gergel
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  2. Konstantin Barkalov
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  3. Ilya Lebedev
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Corresponding author

Correspondence to Konstantin Barkalov .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Trento, Trento, Italy

    Mauro Brunato

  3. Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias Kotsireas

  4. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Gergel, V., Barkalov, K., Lebedev, I. (2019). A Global Optimization Algorithm for Non-Convex Mixed-Integer Problems. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_7

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  • DOI: https://doi.org/10.1007/978-3-030-05348-2_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05347-5

  • Online ISBN: 978-3-030-05348-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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