Comparison of Bio-Inspired Methods with Parameter Adaptation Through Interval Type-2 Fuzzy Logic

  • Frumen Olivas
  • Fevrier Valdez
  • Oscar CastilloEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 749)


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.


Interval type-2 fuzzy logic Ant Colony Optimization Gravitational Search Algorithm Dynamic parameter adaptation 


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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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