Abstract
In the development of this paper we perform a comparison with two bio-inspired methods, Ant Colony Optimization (ACO) and Gravitational Search Algorithm (GSA). Each one of these methods use our 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 try to control their behavior and therefore control their abilities to perform a global or a local search. To test these methods two 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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Olivas, F., Valdez, F., Castillo, O. (2018). Comparison of Bio-Inspired Methods with Parameter Adaptation Through 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_4
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