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Nonlinear Fuzzy Modelling of Dynamic Objects with Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm

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Artificial Intelligence and Soft Computing (ICAISC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12415))

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Abstract

Algorithms based on populations are a very popular family of methods for solving optimization problems. One of the more frequently used representatives of this group is the Particle Swarm Optimization algorithm. The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Such a mechanism exists in the FSHPSO-E algorithm presented in our previous paper. To test it, we used the set of benchmark functions widely adapted in the literature. The results proved effectiveness, efficiency, and scalability of this solution. In this paper, we show the effectiveness of this method in solving practical problems of optimization of fuzzy-neural systems used to model non-linear dynamic objects.

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Author information

Authors and Affiliations

  1. Department of Computational Intelligence, Czestochowa University of Technology, Czestochowa, Poland

    Łukasz Bartczuk & Piotr Dziwiński

  2. Information Technology Institute University of Social Sciences, Łódź, Poland

    Piotr Goetzen

  3. Clark University Worcester, Worcester, USA

    Piotr Goetzen

Authors
  1. Łukasz Bartczuk
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  2. Piotr Dziwiński
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  3. Piotr Goetzen
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Corresponding author

Correspondence to Łukasz Bartczuk .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

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

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Bartczuk, Ł., Dziwiński, P., Goetzen, P. (2020). Nonlinear Fuzzy Modelling of Dynamic Objects with Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12415. Springer, Cham. https://doi.org/10.1007/978-3-030-61401-0_30

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  • DOI: https://doi.org/10.1007/978-3-030-61401-0_30

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