Soft Computing

, Volume 21, Issue 3, pp 667–685 | Cite as

Interval type-2 fuzzy logic for dynamic parameter adaptation in the bat algorithm

  • Jonathan Perez
  • Fevrier Valdez
  • Oscar Castillo
  • Patricia Melin
  • Claudia Gonzalez
  • Gabriela Martinez
Focus

Abstract

We describe in this paper a proposed enhancement of the bat algorithm (BA) using interval type-2 fuzzy logic for dynamically adapting the BA parameters. The BA is a metaheuristic algorithm inspired by the behavior of micro bats that use the echolocation feature for hunting their prey, and this algorithm has been recently applied to different optimization problems obtaining good results. We propose a new method for dynamic parameter adaptation in the BA using interval type-2 fuzzy logic, where an especially design fuzzy system is responsible for determining the optimal values for the parameters of the algorithm. Simulations results on a set of benchmark mathematical functions with the interval type-2 fuzzy bat algorithm outperform the traditional bat algorithm and a type-1 fuzzy variant of BA. The proposed integration of the type-2 fuzzy system into the BA has the goal of improving the performance of BA for the future applicability of the algorithm in more complex optimization problems where higher levels of uncertainty need to be handled, like in the optimization of fuzzy controllers.

Keywords

Optimization Dynamic parameter adaptation Bat algorithm Type-1 fuzzy logic Type-2 fuzzy logic Benchmark mathematical functions 

References

  1. Adorio EP, Diliman UP (2005) MVF—Multivariate test functions library in C for unconstrained global optimization. http://www.geocities.ws/eadorio/mvf.pdf
  2. Amador-Angulo L, Castillo O (2015) Statistical analysis of type-1 and interval type-2 fuzzy logic in dynamic parameter adaptation of the BCO. IFSA-EUSFLAT 2015Google Scholar
  3. Behrouz S, Bahareh B, Parisa G (2015) Fault detection in nonlinear systems based on type-2 fuzzy sets and bat optimization algorithm. J Intell Fuzzy Syst 28(1):179–187Google Scholar
  4. Castillo O, Amador-Angulo L, Castro JR, Garcia-Valdez M (2016) A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems. Inf Sci 354:257–274CrossRefGoogle Scholar
  5. Fister I Jr, Fister D, Yang, XS (2013) A hybrid bat algorithm. Elek 734, trotehniski vestnik 1–7Google Scholar
  6. Gandomi AH, Yang X-S (2014) Chaotic bat algorithm. J Comput Sci 5(2):224–232Google Scholar
  7. Goel N, Gupta D, Goel S (2013) Performance of firefly and bat algorithm for unconstrained optimization problems. Int J Adv Res Comput Sci Softw Eng 3(5):1405–1409Google Scholar
  8. González CI, Castro JR, Melin P, Castillo O (2015) Cuckoo search algorithm for the optimization of type-2 fuzzy image edge detection systems. CEC 2015, Sendai, JapanGoogle Scholar
  9. Gonzalez CI, Patricia Melin JR, Castillo O, Mendoza O (2014) Optimization of interval type-2 fuzzy systems for image edge detection. Appl Soft Comput 13:631–643Google Scholar
  10. Gupta D, Ghafir S (2012) An overview of methods maintaining diversity in genetic algorithms. Int J Emerg Technol Adv Eng 2(5):56–50Google Scholar
  11. Gupta N (2014) Comparative study of type-1 and type-2 fuzzy system. Int J Eng Res Gen Sci 2(4):195–198Google Scholar
  12. Haupt RL, Haupt S (2004) Practical genetic algorithm. Wiley-Interscience a Wiley, HobokenMATHGoogle Scholar
  13. Jun L, Liheng L, Xianyi W (2015) A double-subpopulation variant of the bat algorithm. Appl Math Comput 263:361–377MathSciNetGoogle Scholar
  14. Mirjalili S, Mirjalili SM, Yang X-S (2014) Binary bat algorithm. Neural Comput Appl 25(3):663–681CrossRefGoogle Scholar
  15. Mishra SK (2006) Performance of differential evolution and particle swarm methods on some relatively harder multi-modal benchmark functions. MPRA Mubich Personal RePEc Archive, 10, pp 1–17. https://mpra.ub.uni-muenchen.de/1743/
  16. Olivas F, Valdez F, Castillo O (2013) Particle swarm optimization with dynamic parameter adaptation using interval type-2 fuzzy logic for benchmark mathematical functions. 2013 world congress on nature and biologically inspired computing (NaBIC)Google Scholar
  17. Olivas F, Valdez F, Castillo O (2015) Dynamic parameter adaptation in ant colony optimization using a fuzzy system for TSP problems. In: 2015 conference of the international fuzzy systems association and the European society for fuzzy logic and technology (IFSA-EUSFLAT-15)Google Scholar
  18. Perez J, Castillo O, Valdez F (2015) A new bat algorithm with fuzzy logic for dynamical parameter adaptation and its applicability to fuzzy control design. In: Castillo O, Melin P (eds) Fuzzy logic augmentation of nature-inspired optimization metaheuristics. Springer, Berlin, pp 65–79Google Scholar
  19. Pérez J, Valdez F, Castillo O (2014) Bat algorithm comparison with genetic algorithm using benchmark functions. In: Melin P, Castillo O (eds) Recent advances on hybrid approaches for designing intelligent systems. Springer, Berlin, pp 225–237CrossRefGoogle Scholar
  20. Perez J, Valdez F, Castillo O (2015) A new bat algorithm augmentation using fuzzy logic for dynamical parameter adaptation. In: MICAI-2015: Mexican international conference on artificial intelligence, pp 433–442Google Scholar
  21. Perez J, Valdez F, Castillo O (2015) Modification of the bat algorithm using fuzzy logic for dynamic parameter adaptation. In: CEC2015 IEEE congress on evolutionary computationGoogle Scholar
  22. Perez J, Valdez F, Castillo O (2015) Modification of the bat algorithm using fuzzy logic for dynamical parameter adaptation. In: IEEE congress on evolutionary computation (CEC 2015), pp 464–471Google Scholar
  23. Perez J, Valdez F, Castillo O (2016) Modification of the bat algorithm using type-2 fuzzy logic for dynamical parameter adaptation. Nat Inspir Des Hybrid Intell Syst 667:385–400Google Scholar
  24. Perez J, Valdez F, Castillo O, Roeva O (2016) Bat algorithm with parameter adaptation using interval type-2 fuzzy logic for benchmark mathematical functions. In: Proceedings of 8th international IEEE conference on intelligent systems, pp 120–127Google Scholar
  25. Roeva O, Perez J, Valdez F, Castillo O (2016) InterCriteria analysis of bat algorithm with parameter adaptation using type-1 and interval type-2 fuzzy systems. In: 20th international conference on intuitionistic fuzzy sets, vol 22, no 3, pp 91–105Google Scholar
  26. Yang XS (2010a) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NISCO 2010), pp 67–74Google Scholar
  27. Yang X-S (2010b) BAT algorithm. Nature-inspired metaheuristic algorithms. Luniver Press, UK, pp 97–104Google Scholar
  28. Yang X-S (2012) Bat algorithm for multiobjective optimization. Int J Bio-Inspir Comput 3(5):267–274CrossRefGoogle Scholar
  29. Yang X-S (2013) Bat algorithm: literature review and applications. J Bio-Inspir Comput 5(3):141–149CrossRefGoogle Scholar
  30. Yang X-S (2014) Nature-inspired optimization algorithm. Middlesex University London, Elsevier, LondonMATHGoogle Scholar
  31. Yılmaz S, Kücüksille EU (2015) A new modification approach on bat algorithm for solving optimization problems. Appl Soft Comput 259–275Google Scholar
  32. Zadeh L (1965) Fuzzy sets. Inform Control 338–353Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Jonathan Perez
    • 1
  • Fevrier Valdez
    • 1
  • Oscar Castillo
    • 1
  • Patricia Melin
    • 1
  • Claudia Gonzalez
    • 1
  • Gabriela Martinez
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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