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Optimization of Fuzzy Logic Controllers with Distributed Bio-Inspired Algorithms

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Recent Advances of Hybrid Intelligent Systems Based on Soft Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 915))

Abstract

Optimization problems are present in many fields of science, engineering, and industry. In most cases, optimization problems involve a large number of complex decision variables. Often, the application of fuzzy systems to traditional control strategies requires the use of optimization techniques to define the parameters of the membership functions capturing the designer’s experience. Currently, bioinspired algorithms are one of the most widely used methods for fuzzy system optimization. In this paper, we review the literature on the application of different bioinspired techniques such as Genetic Algorithms, Ant Colony Optimization, Particle Swarm Optimization, Galaxy Swarm Optimization, Harmony Search, Firefly, Differential Evolution, to optimize the parameters of the fuzzy controllers. We found particular trends in the current literature, with many methods using dynamic parameter adaptation, hybrids, distributed algorithms, and type-2 fuzzy logic.

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Authors and Affiliations

  1. Instituto Tecnológico de Tijuana, Tijuana, BC, Mexico

    Alejandra Mancilla, Oscar Castillo & Mario García Valdez

Authors
  1. Alejandra Mancilla
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  2. Oscar Castillo
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  3. Mario García Valdez
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Corresponding author

Correspondence to Alejandra Mancilla .

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Editors and Affiliations

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

    Patricia Melin

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

    Oscar Castillo

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

    Janusz Kacprzyk

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Mancilla, A., Castillo, O., Valdez, M.G. (2021). Optimization of Fuzzy Logic Controllers with Distributed Bio-Inspired Algorithms. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Recent Advances of Hybrid Intelligent Systems Based on Soft Computing. Studies in Computational Intelligence, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-58728-4_1

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