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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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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