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A New Way of Decomposing Search Domain in a Global Optimization Problem

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Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17) (IITI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 679))

Abstract

This paper deals with a new method for decomposing search domain in a global optimization problem. Proposed method was designed for parallel population algorithms but also can be used as a diversification tool in sequential algorithms. New decomposition technique was compared with a traditional approach by means of numeric experiments with a use of multi-dimensional benchmark optimization functions and Mind Evolutionary Computation algorithm. Results of the experiments demonstrate the superiority of new technique over a canonical approach which resulted in a higher quality of obtained solutions.

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Author information

Authors and Affiliations

  1. Bauman MSTU, Moscow, Russia

    Maxim Sakharov & Anatoly Karpenko

Authors
  1. Maxim Sakharov
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  2. Anatoly Karpenko
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Corresponding author

Correspondence to Maxim Sakharov .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR labs), Auburn, Washington, USA

    Ajith Abraham

  2. Rostov State Transport University , Rostov-on-Don, Russia

    Sergey Kovalev

  3. Bauman Moscow State Technical University , Moscow, Russia

    Valery Tarassov

  4. VSB-Technical University of Ostrava , Ostrava, Czech Republic

    Vaclav Snasel

  5. Department Electrical Power Engineering, Technical University of Varna, Varna, Bulgaria

    Margreta Vasileva

  6. Rostov State Transport University , Rostov-on-Don, Russia

    Andrey Sukhanov

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Sakharov, M., Karpenko, A. (2018). A New Way of Decomposing Search Domain in a Global Optimization Problem. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Vasileva, M., Sukhanov, A. (eds) Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17). IITI 2017. Advances in Intelligent Systems and Computing, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-319-68321-8_41

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  • DOI: https://doi.org/10.1007/978-3-319-68321-8_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68320-1

  • Online ISBN: 978-3-319-68321-8

  • eBook Packages: EngineeringEngineering (R0)

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