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Metaheuristics in Physical Processes Optimization

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Information Technology, Systems Research, and Computational Physics (ITSRCP 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 945))

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Abstract

The subject of this work is applying the artificial neural network (ANN) taught using two metaheuristics - the firefly algorithm (FA) and properly prepared evolutionary algorithm (EA) - to find the approximate solution of the Wessinger’s equation, which is a nonlinear, first order, ordinary differential equation. Both methods were compared as an ANN training tool. Then, application of this method in selected physical processes is discussed.

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Acknowledgments

This work was supported by the Systems Research Institute of the Polish Academy of Sciences and is extended version of paper presented at 3rd Conference on Information Technology, Systems Research and Computational Physics, 2–5 July 2018, Cracow, Poland [21].

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Authors and Affiliations

  1. Centre of Information Technology for Data Analysis Methods, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Tomasz Rybotycki

Authors
  1. Tomasz Rybotycki
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Correspondence to Tomasz Rybotycki .

Editor information

Editors and Affiliations

  1. Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland

    Piotr Kulczycki

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Faculty of Engineering Sciences, Széchenyi István University, Győr, Hungary

    László T. Kóczy

  4. Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia

    Radko Mesiar

  5. Department of Electronic Systems, Aalborg University, Aalborg, Denmark

    Rafal Wisniewski

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Rybotycki, T. (2020). Metaheuristics in Physical Processes Optimization. In: Kulczycki, P., Kacprzyk, J., Kóczy, L., Mesiar, R., Wisniewski, R. (eds) Information Technology, Systems Research, and Computational Physics. ITSRCP 2018. Advances in Intelligent Systems and Computing, vol 945. Springer, Cham. https://doi.org/10.1007/978-3-030-18058-4_11

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