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Optimization of Membership Function Parameters for Fuzzy Controllers of an Autonomous Mobile Robot Using the Firefly Algorithm

  • Marylu L. Lagunes
  • Oscar CastilloEmail author
  • Jose Soria
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 749)

Abstract

Bio-Inspired Algorithms are effective for solving optimization problems, in particular for finding the appropriate parameter values for the membership functions used in fuzzy control. Fuzzy controllers are widely used in engineering, industrial, and medical solutions and other fields. Fuzzy models help to represent informal, unstructured abstract knowledge into formal mathematical models. In this paper the firefly algorithm is used to optimize fuzzy controllers for autonomous mobile robots. In this work optimization of parameters of the membership functions in fuzzy control systems allows a better performance of the actuators that are controlling an autonomous robot. This article will explain the proposed methodology for the optimization of parameters of membership functions of a tracking fuzzy controller for a mobile autonomous robot using the firefly algorithm.

Keywords

Firefly algorithm Membership functions Optimization problems Fuzzy system 

References

  1. 1.
    D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    I. Fister, I. Fister Jr, J. Brest, V. Zumer, Memetic artificial bee colony algorithm for large-scale global optimization, in IEEE Congress on Evolutionary Computing, pp. 1–8 (2012)Google Scholar
  3. 3.
    X. Yang, S. Deb, Cucko search via levy flights, in World Congress on Nature & Biologically Inspired Computing, pp. 210–214 (2009)Google Scholar
  4. 4.
    X. Yang, A new metaheuristic bat-inspired algorithm. Stud. Comput. Intell. 284, 65–74 (2010)zbMATHGoogle Scholar
  5. 5.
    A. Gandomi, A. Alavi, Krill herd: a new bio-inspired optimization algorithm. Commun. Nonlinear Sci. Numer. Simul. 17(12), 4831–4845 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    G. Aluja, A. Kaufmann, Operational management techniques for the treatment of uncertainty (Hispano Europea, Barcelona, 1987)Google Scholar
  7. 7.
    A. Kaufmann, J. Gil Aluja, Theory of expertons and fuzzy logic, in Fuzzy Sets and Systems, España, Milladoiro, pp. 295–304 (1986)Google Scholar
  8. 8.
    L.A. Zadeh, Fuzzy logic. Computer 21(4), 83–93 (1988)CrossRefGoogle Scholar
  9. 9.
    L. Zadeh, “Fuzzy sets. Inf Control 8, 338–353 (1965). Department of Electrical Engineering and Electronics Research LaboratoryGoogle Scholar
  10. 10.
    B. Gonzalez, F. Valdez, P. Melin, A gravitational search algorithm using type-2 fuzzy logic for parameter adaptation, in Nature-Inspired Design of Hybrid Intelligent Systems (Springer, Tijuana, Mexico, 2017), pp. 127–138Google Scholar
  11. 11.
    L. Rodriguez, O. Castillo, J. Soria, A study of parameters of the grey wolf optimizer algorithm for dynamic adaptation with fuzzy logic, in Nature-Inspired Design of Hybrid Intelligent Systems (Springer, Tijuana, Mexico, 2017), pp. 371–390Google Scholar
  12. 12.
    E. Mendez, O. Castillo, J. Soria, A. Sandollah, Fuzzy dynamic adaptation of parameters in the water cycle algorithm, in Nature-Inspired Design of Hybrid Intelligent Systems (Springer, Tijuana, Mexico, 2017), pp. 297–311Google Scholar
  13. 13.
    M. Crepinsek, M. Mernik, S. Liu, Analysis of exploration and exploitation in evolutionary algorithms by ancestry trees. Int. J. Innovative Comput. Appl. 3(1), 11–19 (2011)CrossRefzbMATHGoogle Scholar
  14. 14.
    K. Tashkova, J. Silc, N. Atanasova, S. Dzeroski, Parameter estimation in a nonlinear dynamic model of an aquatic ecosystem with meta-heuristic optimization. Ecol Model 226, 36–61 (2012)CrossRefGoogle Scholar
  15. 15.
    D. Goldberg, Genetic Algorithms in Search (1989)Google Scholar
  16. 16.
    M. Dorigo, C. Blum, Ant colony optimization theory: a survey. Theoret. Comput. Sci. 334, 243–278 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    P. Korosec, J. Silc, B. Filipic, The differential ant-stigmergy algorithm. Inf. Sci., pp. 82–97 (2012)Google Scholar
  18. 18.
    J. Kennedy, R. Eberthart, The particle swarm optimization: social adaptation in information processing, in New Ideas in Optimization, pp. 379–387 (1999)Google Scholar
  19. 19.
    B. Jakimovski, B. Meyer, E. Maehle, Firefly flashing synchronization as inspiration for self-synchronization of walking robot gait patterns using a decentralized robot control architecture, in Architecture of Computing Systems, pp. 61–72 (2010)Google Scholar
  20. 20.
    S. Severin, J. Rossmann, A comparison of different metaheuristic algorithms for optimizing blended PTP movements for industrial robots, in Intelligent Robotics and Applications, pp. 321–330 (2012)Google Scholar
  21. 21.
    A. Chatterjee, G. Mahanti, A. Chatterjee, Design of a fully digital controlled reconfigurable switched beam concentric ring array antenna using firefly and particle swarm optimization algorithm. Prog. Electromagn. Res., pp. 113–131 (2012)Google Scholar
  22. 22.
    Y. Zhang, L. Wu, A novel method for rigid image registration based on firefly algorithm. Int. J. Res. Rev. Soft Intell. Comput. 2(2), 141–146 (2012)MathSciNetGoogle Scholar
  23. 23.
    B. Basu, G. Mahanti, Firefly and artificial beescolony algorithm for synthesis of scanned and broadside linear array antenna. Prog. Electromagn. Res., pp. 169–190 (2011)Google Scholar
  24. 24.
    G. Giannakouris, V. Vassiliadis, G. Dounias, Experimental study on a hybrid nature-inspired algorithm for financial portfolio optimization, in Artificial Intelligence: Theories, Models and Applications, pp. 101–111 (2010)Google Scholar
  25. 25.
    A. Santos, H. Campos Velho, E. Luz, S. Freitas, G. Grell, M. Gan, Firefly optimization to determine the precipitation field on South America, Inverse Problems in Science and Engineering, pp. 1–16 (2013)Google Scholar
  26. 26.
    X. Yang, Firefly algorithm, in Nature-Inspired Metaheuristic Algorithms, pp. 79–90 (2008)Google Scholar
  27. 27.
    M. Sanchez, O. Castillo, J. Castro, Generalized Type-2 Fuzzy Systems for controlling a mobile robot and a performance comparison with interval type-2 and type-1 fuzzy systems. Expert Syst. Appl. 42, 5904–5914 (2015)CrossRefGoogle Scholar
  28. 28.
    O. Castillo, P. Melin, O. Montiel, R. Sepulveda, W. Pedrycz, Theoretical Advances and Applications of Fuzzy Logic and Soft Computing (Springer, Tijuana, BC, 2007)CrossRefzbMATHGoogle Scholar
  29. 29.
    R. Martinez, O. Castillo, L.T. Aguilar, Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms. Inf. Sci. 179(13), 2158–2174 (2009)CrossRefzbMATHGoogle Scholar
  30. 30.
    C. Soto, F. Valdez,O. Castillo, A review of dynamic parameter adaptation methods for the firefly algorithm, in Nature-Inspired Design of Hybrid Intelligent Systems (Springer, Tijuana, BC, 2007), pp. 285–295Google Scholar
  31. 31.
    L. Astudillo, P. Melin, O. Castillo, Chemical Optimization Algorithm for Fuzzy Controller Design, Tijuana (Springer, Mexico, 2014)CrossRefzbMATHGoogle Scholar
  32. 32.
    A. Sombra, F. Valdez, P. Melin, O. Castillo, A new gravitational search algorithm using fuzzy logic to parameter adaptation, in IEEE Congress on Evolutionary Computation, Cancun, México, pp. 1068–1074 (2013)Google Scholar
  33. 33.
    F. Valdez, P. Melin, O. Castillo, Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making, in IEEE International Conference on Fuzzy Systems, pp. 2114–2119 (2009)Google Scholar
  34. 34.
    L. Aguilar, P. Melin, O. Castillo, Intelligent control of a stepping motor drive using a hybrid neuro-fuzzy ANFIS approach. Appl. Soft Comput. 3(3), 209–219Google Scholar
  35. 35.
    P. Melin, O. Castillo, Intelligent control of complex electrochemical systems with a neuro-fuzzy-genetic approach, IEEE Trans. Ind. Electron. 48(5), 951–955Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Marylu L. Lagunes
    • 1
  • Oscar Castillo
    • 1
    Email author
  • Jose Soria
    • 1
  1. 1.Tijuana Institute of TechnologyTijuanaMexico

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