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Insight into Adaptive Differential Evolution Variants with Unconventional Randomization Schemes

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Book cover Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing (SEMCCO 2019, FANCCO 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1092))

Abstract

The focus of this work is the deeper insight into arising serious research questions connected with the growing popularity of combining metaheuristic algorithms and chaotic sequences showing quasi-periodic patterns. This paper reports an analysis of population dynamics by linking three elements like distribution of the results, population diversity, and differences between strategies of Differential Evolution (DE). Experiments utilize two frequently studied self-adaptive DE versions, which are simpler jDE and SHADE, further an original DE variant for comparisons, and totally ten chaos-driven quasi-random schemes for the indices selection in the DE. All important performance characteristics and population diversity are recorded and analyzed for the CEC 2015 benchmark set in 30D.

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Acknowledgement

Authors RS, AV, TK and MP acknowledge the support of project No. LO1303 (MSMT-7778/2014) by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme, further the project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 under the European Regional Development Fund. Authors AV and TK also acknowledge the Internal Grant Agency of Tomas Bata University under the project No. IGA/CebiaTech/2019/002. This work is also based upon support by COST Action CA15140 (ImAppNIO), and the resources of A.I.Lab at the Faculty of Applied Informatics, TBU in Zlin (ailab.fai.utb.cz). Finally, Author Ivan Zelinka acknowledges the support of grant SGS 2019/137, VSB-Technical University of Ostrava.

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Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, 760 01, Zlin, Czech Republic

    Roman Senkerik, Adam Viktorin, Tomas Kadavy & Michal Pluhacek

  2. Faculty of Electrical Engineering and Computer Science, Technical University of Ostrava, 17. listopadu 15, Ostrava, Czech Republic

    Ivan Zelinka

Authors
  1. Roman Senkerik
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  2. Adam Viktorin
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  3. Tomas Kadavy
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  4. Michal Pluhacek
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  5. Ivan Zelinka
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Corresponding author

Correspondence to Roman Senkerik .

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Editors and Affiliations

  1. University of Maribor, Maribor, Slovenia

    Aleš Zamuda

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  3. Division of Control and Instrumentation, Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  4. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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Senkerik, R., Viktorin, A., Kadavy, T., Pluhacek, M., Zelinka, I. (2020). Insight into Adaptive Differential Evolution Variants with Unconventional Randomization Schemes. In: Zamuda, A., Das, S., Suganthan, P., Panigrahi, B. (eds) Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing. SEMCCO FANCCO 2019 2019. Communications in Computer and Information Science, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-37838-7_16

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  • DOI: https://doi.org/10.1007/978-3-030-37838-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37837-0

  • Online ISBN: 978-3-030-37838-7

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