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International Conference on Learning and Intelligent Optimization

LION 2017: Learning and Intelligent Optimization pp 383–388Cite as

Emmental-Type GKLS-Based Multiextremal Smooth Test Problems with Non-linear Constraints

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10556))

Abstract

In this paper, multidimensional test problems for methods solving constrained Lipschitz global optimization problems are proposed. A new class of GKLS-based multidimensional test problems with continuously differentiable multiextremal objective functions and non-linear constraints is described. In these constrained problems, the global minimizer does not coincide with the global minimizer of the respective unconstrained test problem, and is always located on the boundaries of the admissible region. Two types of constraints are introduced. The possibility to choose the difficulty of the admissible region is available.

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Acknowledgements

This work was supported by the project No. 15-11-30022 “Global optimization, supercomputing computations, and applications” of the Russian Science Foundation.

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Authors and Affiliations

  1. University of Calabria, Rende (CS), Italy

    Ya. D. Sergeyev, D. E. Kvasov & M. S. Mukhametzhanov

  2. Lobachevsky State University, Nizhni Novgorod, Russia

    Ya. D. Sergeyev, D. E. Kvasov & M. S. Mukhametzhanov

Authors
  1. Ya. D. Sergeyev
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  2. D. E. Kvasov
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  3. M. S. Mukhametzhanov
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Corresponding author

Correspondence to M. S. Mukhametzhanov .

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Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Calabria, Rende, Italy

    Dmitri E. Kvasov

  3. University of Calabria, Rende, Italy

    Yaroslav D. Sergeyev

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Sergeyev, Y.D., Kvasov, D.E., Mukhametzhanov, M.S. (2017). Emmental-Type GKLS-Based Multiextremal Smooth Test Problems with Non-linear Constraints. In: Battiti, R., Kvasov, D., Sergeyev, Y. (eds) Learning and Intelligent Optimization. LION 2017. Lecture Notes in Computer Science(), vol 10556. Springer, Cham. https://doi.org/10.1007/978-3-319-69404-7_35

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  • DOI: https://doi.org/10.1007/978-3-319-69404-7_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69403-0

  • Online ISBN: 978-3-319-69404-7

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