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Improving the Convergence of Differential Evolution

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Numerical Analysis and Its Applications (NAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10187))

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Abstract

A new variant of differential evolution (DE) algorithm with a selection of mutation strategy based on the mutant point distance (DEMD) is proposed. Three DEMD variants are compared with state-of-the-art DE variants on CEC 2015 problems at four dimension levels. The results show that one of proposed DEMD variants performs best in 35% of the problems compared to the other examined DE algorithms.

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Acknowledgments

This work was supported by University of Ostrava from the project SGS08/UVAFM/2016.

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Authors and Affiliations

  1. Department of Computer Science, University of Ostrava, 30. dubna 22, 701 03, Ostrava, Czech Republic

    Petr Bujok

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  1. Petr Bujok
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Correspondence to Petr Bujok .

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Editors and Affiliations

  1. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Dimov

  2. Department of Applied Analysis, Eötvös Loránd University, Budapest, Hungary

    István Faragó

  3. Department of Applied Mathematics and Statistics, University of Rousse, Ruse, Bulgaria

    Lubin Vulkov

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Bujok, P. (2017). Improving the Convergence of Differential Evolution. In: Dimov, I., Faragó, I., Vulkov, L. (eds) Numerical Analysis and Its Applications. NAA 2016. Lecture Notes in Computer Science(), vol 10187. Springer, Cham. https://doi.org/10.1007/978-3-319-57099-0_26

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  • DOI: https://doi.org/10.1007/978-3-319-57099-0_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57098-3

  • Online ISBN: 978-3-319-57099-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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