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Differential Evolution with Dynamic Adaptation of Parameters for the Optimization of Fuzzy Controllers

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Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 601))

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Abstract

The proposal described in this paper uses the Differential Evolution (DE) algorithm as an optimization method in which we want to dynamically adapt its parameters using fuzzy logic control systems, with the goal that the fuzzy system calculates the optimal parameter values of the DE algorithm to find better results, depending on the type of problems the DE is applied. In this case we consider a fuzzy system to dynamically change the F and CR variables.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Ochoa, Oscar Castillo & José Soria

Authors
  1. Patricia Ochoa
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  2. Oscar Castillo
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  3. José Soria
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Corresponding author

Correspondence to Oscar Castillo .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

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

    Janusz Kacprzyk

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Ochoa, P., Castillo, O., Soria, J. (2015). Differential Evolution with Dynamic Adaptation of Parameters for the Optimization of Fuzzy Controllers. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. Studies in Computational Intelligence, vol 601. Springer, Cham. https://doi.org/10.1007/978-3-319-17747-2_44

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  • DOI: https://doi.org/10.1007/978-3-319-17747-2_44

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17746-5

  • Online ISBN: 978-3-319-17747-2

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