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Parallel Algorithm for Solving Constrained Global Optimization Problems

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Parallel Computing Technologies (PaCT 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10421))

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Abstract

This work considers a parallel algorithm for solving multiextremal problems with non-convex constraints. The distinctive feature of this algorithm, which does not use penalty functions, is the separate consideration of each problem constraint. The search process can be conducted by reducing the original multidimensional problem to a number of related one-dimensional problems and solving this set of problems in parallel. An experimental assessment of parallel algorithm efficiency was conducted by finding the numeric solution to several hundred randomly generated multidimensional multiextremal problems with non-convex constraints.

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Acknowledgements

The 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

    Konstantin Barkalov & Ilya Lebedev

Authors
  1. Konstantin Barkalov
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  2. Ilya Lebedev
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Corresponding author

Correspondence to Konstantin Barkalov .

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Editors and Affiliations

  1. Russian Academy of Sciences, Novosibirsk, Russia

    Victor Malyshkin

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Barkalov, K., Lebedev, I. (2017). Parallel Algorithm for Solving Constrained Global Optimization Problems. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2017. Lecture Notes in Computer Science(), vol 10421. Springer, Cham. https://doi.org/10.1007/978-3-319-62932-2_38

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  • DOI: https://doi.org/10.1007/978-3-319-62932-2_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62931-5

  • Online ISBN: 978-3-319-62932-2

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