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Solving a set of global optimization problems by the parallel technique with uniform convergence

Author

Listed:
  • Konstantin Barkalov

    (Lobachevsky State University of Nizhni Novgorod)

  • Roman Strongin

    (Lobachevsky State University of Nizhni Novgorod)

Abstract

In this paper, we consider solving a set of global optimization problems in parallel. The proposed novel algorithm provides uniform convergence to the set of solutions for all problems treated simultaneously. The current accuracy for each particular solution is estimated by the difference in each coordinate from the point of global decision. The main statement is given in the corresponding theorem. For the sake of illustration some computational results with hundreds of multidimensional global problems are provided.

Suggested Citation

  • Konstantin Barkalov & Roman Strongin, 2018. "Solving a set of global optimization problems by the parallel technique with uniform convergence," Journal of Global Optimization, Springer, vol. 71(1), pages 21-36, May.
    • Handle: RePEc:spr:jglopt:v:71:y:2018:i:1:d:10.1007_s10898-017-0555-4
    DOI: 10.1007/s10898-017-0555-4
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    References listed on IDEAS

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    1. Konstantin Barkalov & Victor Gergel, 2016. "Parallel global optimization on GPU," Journal of Global Optimization, Springer, vol. 66(1), pages 3-20, September.
    2. Remigijus Paulavičius & Yaroslav Sergeyev & Dmitri Kvasov & Julius Žilinskas, 2014. "Globally-biased Disimpl algorithm for expensive global optimization," Journal of Global Optimization, Springer, vol. 59(2), pages 545-567, July.
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    Cited by:

    1. Lera, Daniela & Posypkin, Mikhail & Sergeyev, Yaroslav D., 2021. "Space-filling curves for numerical approximation and visualization of solutions to systems of nonlinear inequalities with applications in robotics," Applied Mathematics and Computation, Elsevier, vol. 390(C).

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