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Dynamic Parameter Adaptation Based on Using Interval Type-2 Fuzzy Logic in Bio-inspired Optimization Methods

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Book cover Innovations in Bio-Inspired Computing and Applications (IBICA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 939))

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Abstract

In this paper we perform a comparison of the use of type-2 fuzzy logic in two bio-inspired methods: Ant Colony Optimization (ACO) and Gravitational Search Algorithm (GSA). Each of these methods is enhanced with a methodology for parameter adaptation using interval type-2 fuzzy logic, where based on some metrics about the algorithm, like the percentage of iterations elapsed or the diversity of the population, we aim at controlling their behavior and therefore control their abilities to perform a global or a local search. To test these methods two benchmark control problems were used in which a fuzzy controller is optimized to minimize the error in the simulation with nonlinear complex plants.

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Calzada Tecnologico s/n, Tomas Aquino, 22379, Tijuana, Mexico

    Oscar Castillo

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  1. Oscar Castillo
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Correspondence to Oscar Castillo .

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Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

  3. Department of Applied Science and Engineering, Indian Institute of Technology, Roorkee, India

    Millie Pant

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Castillo, O. (2019). Dynamic Parameter Adaptation Based on Using Interval Type-2 Fuzzy Logic in Bio-inspired Optimization Methods. In: Abraham, A., Gandhi, N., Pant, M. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2018. Advances in Intelligent Systems and Computing, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-030-16681-6_1

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