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State Flipping Based Hyper-Heuristic for Hybridization of Nature Inspired Algorithms

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Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10245))

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Abstract

The paper presents a novel hyper-heuristic strategy for hybridization of nature inspired algorithms. The strategy is based on switching the state of agents using a logistic probability function, which depends upon the fitness rank of an agent. A case study using two nature inspired algorithms (Artificial Bee Colony (ABC) and Krill Herding (KH)) and eight optimization problems (Ackley Function, Bukin Function N.6, Griewank Function, Holder Table Function, Levy Function, Schaffer Function N.2, Schwefel Function, Shubert Function) is presented. The results show a superiority of the proposed hyper-heuristic (mean end-rank for hybrid algorithm is 1.435 vs. 2.157 for KH and 2.408 for ABC).

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Authors and Affiliations

  1. Department of Software Engineering, Kaunas University of Technology, Studentu 50, Kaunas, Lithuania

    Robertas Damaševičius

  2. Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100, Gliwice, Poland

    Marcin Woźniak

Authors
  1. Robertas Damaševičius
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  2. Marcin Woźniak
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Correspondence to Marcin Woźniak .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Damaševičius, R., Woźniak, M. (2017). State Flipping Based Hyper-Heuristic for Hybridization of Nature Inspired Algorithms. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10245. Springer, Cham. https://doi.org/10.1007/978-3-319-59063-9_30

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  • DOI: https://doi.org/10.1007/978-3-319-59063-9_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59062-2

  • Online ISBN: 978-3-319-59063-9

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