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A Comparative Study of Dynamic Adaptation of Parameters in the GWO Algorithm Using Type-1 and Interval Type-2 Fuzzy Logic

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Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 749))

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Abstract

The main goal of this paper is to present a comparative study of dynamic adjustment of parameters in the Grey Wolf Optimizer algorithm using type-1 and interval type-2 fuzzy logic respectively. We proposed the fuzzy inference system for both types of fuzzy logic and we present the performance of these proposed methods with a set of 13 benchmark functions that we are presenting in this paper.

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Correspondence to Oscar Castillo .

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Rodríguez, L., Castillo, O., García, M., Soria, J. (2018). A Comparative Study of Dynamic Adaptation of Parameters in the GWO Algorithm Using Type-1 and Interval Type-2 Fuzzy Logic. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications. Studies in Computational Intelligence, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-71008-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-71008-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71007-5

  • Online ISBN: 978-3-319-71008-2

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