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Comparing Two Approaches for Solving Constrained Global Optimization Problems

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Learning and Intelligent Optimization (LION 2017)

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

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Abstract

In the present study, a method for solving the multiextremal problems with non-convex constrains without the use of the penalty or barrier functions is considered. This index method is featured by a separate accounting for each constraint. The check of the constraint fulfillment sequentially performed in every iteration point is terminated upon the first constraint violation occurs. The results of numerical comparing of the index method with the penalty function one are presented. The comparing has been carried out by means of the numerical solving of several hundred multidimensional multiextremal problems with non-convex constrains generated randomly.

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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

    Konstantin Barkalov & Ilya Lebedev

Authors
  1. Konstantin Barkalov
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  2. Ilya Lebedev
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Correspondence to Konstantin Barkalov .

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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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Barkalov, K., Lebedev, I. (2017). Comparing Two Approaches for Solving Constrained Global Optimization Problems. 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_22

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  • DOI: https://doi.org/10.1007/978-3-319-69404-7_22

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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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