Soft Computing

, Volume 22, Issue 2, pp 571–594 | Cite as

A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers

  • Leticia Amador-Angulo
  • Oscar Castillo
Methodologies and Application


This paper presents a new fuzzy bee colony optimization method to find the optimal distribution of the membership functions in the design of fuzzy controllers for complex nonlinear plants. We used interval type-2 and type-1 fuzzy logic systems in dynamically adapting the alpha and beta parameter values of the bee colony optimization algorithm (BCO). Simulation results with a type-1 fuzzy logic controller for benchmark control plants are presented. The advantage of using interval type-2 fuzzy logic systems for dynamic adjustment of parameters in BCO applied in fuzzy controller design is verified with two benchmark problems. We considered different levels and types of noise in the simulations to analyze the approach of interval type-2 fuzzy logic systems to find the best values of alpha and beta for BCO when applied in the design of fuzzy controllers.


Interval type-2 fuzzy logic system Fuzzy controller Bio-inspired algorithm Footprint uncertainty Bee colony optimization 



This research work did not receive funding.

Compliance with ethical standards

Conflict of interest

All the authors in the paper have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Division of Graduate StudiesTijuana Institute of TechnologyTijuanaMexico

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