Skip to main content
SpringerLink
Log in
Book cover

North American Fuzzy Information Processing Society Annual Conference

NAFIPS 2017: Fuzzy Logic in Intelligent System Design pp 61–72Cite as

Comparative Study of Metrics That Affect in the Performance of the Bee Colony Optimization Algorithm Through Interval Type-2 Fuzzy Logic Systems

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 648))

Abstract

A comparative study of different proposed methods using interval type-2 fuzzy logic systems (IT2FLS) to find optimal α and β values in a Bee Colony Optimization Algorithm (BCO) applied to the stabilization of the trajectory in an autonomous mobile robot (AMR) is presented. Three metrics are analyzed for finding the optimal values that affect in the efficiency of the BCO algorithm. Perturbation is added in the model. Simulation results indicate that the MSE error is an important metric for determine the optimal values in the effective of the execution in the BCO algorithm.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Abdelbar, A.M., Abdelshahid, S., Wunsch, D.C.: Fuzzy PSO: a generalization of particle swarm optimization. In: Proceedings of 2005 IEEE International Joint Conference on Neural Networks, IJCNN 2005, vol. 2, pp. 1086–1091. IEEE, July 2005

    Google Scholar 

  2. Abraham, A., Nath, B.: Evolutionary design of fuzzy control systems-an hybrid approach. In: The Sixth International Conference on Control, Automation, Robotics and Vision (ICARCV 2000), CD-ROM Proceeding, Wang JL (Ed.), ISBN (vol. 1220499864), December 2000

    Google Scholar 

  3. Amador-Angulo, L., Mendoza, O., Castro, J.R., Rodríguez-Díaz, A., Melin, P., Castillo, O.: Fuzzy sets in dynamic adaptation of parameters of a bee colony optimization for controlling the trajectory of an autonomous mobile robot. Sensors 16(9), 1458 (2016)

    Article  Google Scholar 

  4. Bäck, T., Schwefel, H.P.: An overview of evolutionary algorithms for parameter optimization. Evol. Comput. 1(1), 1–23 (1993)

    Article  Google Scholar 

  5. Bidar, M., Kanan, H.R.: Modified firefly algorithm using fuzzy tuned parameters. In: 2013 13th Iranian Conference on Fuzzy Systems (IFSC), pp. 1–4. IEEE, August 2013

    Google Scholar 

  6. Biesmeijer, J.C., Seeley, T.D.: The use of waggle dance information by honey bees throughout their foraging careers. Behav. Ecol. Sociobiol. 59(1), 133–142 (2005)

    Article  Google Scholar 

  7. Brown, S.C., Passino, K.M.: Intelligence control for an acrobat. J. Intell. Robot. Syst. 18(3), 209–248 (1997)

    Article  Google Scholar 

  8. Buckley, J.J., Hayashi, Y.: Fuzzy genetic algorithm and applications. Fuzzy Sets Syst. 61(2), 129–136 (1994)

    Article  MathSciNet  Google Scholar 

  9. Chong, C.S., Low, M.Y.H., Sivakumar, A.I., Gay, K.L.: A bee colony optimization algorithm to job shop scheduling. In: Proceedings of the 2006 Winter Simulation Conference, pp. 13–25 (2006)

    Google Scholar 

  10. Dyler, F.C.: The biology of the dance language. Annu. Rev. Entomol. 47, 917–949 (2002)

    Article  Google Scholar 

  11. Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29(1), 17–35 (2013)

    Article  Google Scholar 

  12. Guerrero, M., Castillo, O., García, M.: Fuzzy dynamic parameters adaptation in the Cuckoo Search Algorithm using fuzzy logic. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 441–448. IEEE, May 2015

    Google Scholar 

  13. Hassanzadeh, T., Kanan, H.R.: Fuzzy FA: a modified firefly algorithm. Appl. Artif. Intell. 28(1), 47–65 (2014)

    Article  Google Scholar 

  14. Juang, C.F., Chang, P.H.: Designing fuzzy-rule-based systems using continuous ant-colony optimization. IEEE Trans. Fuzzy Syst. 18(1), 138–149 (2010)

    Article  Google Scholar 

  15. Juang, C.F., Lu, C.M., Lo, C., Wang, C.Y.: Ant colony optimization algorithm for fuzzy controller design and its FPGA implementation. IEEE Trans. Industr. Electron. 55(3), 1453–1462 (2008)

    Article  Google Scholar 

  16. Karr, C.L., Gentry, E.J.: Fuzzy control of pH using genetic algorithms. IEEE Trans. Fuzzy Syst. 1(1), 46 (1993)

    Article  Google Scholar 

  17. Liu, J., Lampinen, J.: A fuzzy adaptive differential evolution algorithm. In: Proceedings of 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering, TENCON 2002, vol. 1, pp. 606–611. IEEE, October 2002

    Google Scholar 

  18. Lee, C.C.: Fuzzy logic in control systems: fuzzy logic controller. I. IEEE Trans. Syst. Man Cybern. 20(2), 404–418 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  19. Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194(36), 3902–3933 (2005)

    Article  MATH  Google Scholar 

  20. Lučić, P., Teodorović, D.: Computing with bees: attacking complex transportation engineering problems. Int. J. Artif. Intell. Tools 12(3), 375–394 (2003)

    Article  Google Scholar 

  21. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with fuzzy logic controller. Int. J. Man-Mach. Stud. 7(1), 1–13 (1975)

    Article  MATH  Google Scholar 

  22. Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. In: Proceedings of the Institution of Electrical Engineers, vol. 121, no. 12, pp. 1585–1588. IET, December 1974

    Google Scholar 

  23. Mendel, J.M.: Advances in type-2 fuzzy sets and systems. Inf. Sci. 177(1), 84–110 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  24. Mendel, J.M., John, R.I., Liu, F.: Interval type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Syst. 14(6), 808–821 (2006)

    Article  Google Scholar 

  25. Parpinelli, R.S., Lopes, H.S.: New inspirations in swarm intelligence: a survey. Int. J. Bio-Inspired Comput. 3(1), 1–16 (2011)

    Article  Google Scholar 

  26. Pandiarajan, K., Babulal, C.K.: Fuzzy harmony search algorithm based optimal power flow for power system security enhancement. Int. J. Electr. Power Energy Syst. 78, 72–79 (2016)

    Article  Google Scholar 

  27. Pérez, J., Valdez, F., Castillo, O.: A new bat algorithm with fuzzy logic for dynamical parameter adaptation and its applicability to fuzzy control design. In: Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics, pp. 65–79. Springer International Publishing (2015)

    Google Scholar 

  28. Saeidi-Khabisi, F.S., Rashedi, E.: Fuzzy gravitational search algorithm. In: 2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE), pp. 156–160. IEEE, October 2012

    Google Scholar 

  29. Sharma, K.D., Chatterjee, A., Rakshit, A.: Design of a hybrid stable adaptive fuzzy controller employing Lyapunov theory and harmony search algorithm. IEEE Trans. Control Syst. Technol. 18(6), 1440–1447 (2010)

    Google Scholar 

  30. Teodorović, D.: Swarm intelligence systems for transportation engineering: principles and applications. Transp. Res. Pt. C-Emerg. Technol. 116, 651–782 (2008)

    Article  Google Scholar 

  31. Tolabi, H.B., Ali, M.H., Rizwan, M.: Simultaneous reconfiguration, optimal placement of DSTATCOM, and photovoltaic array in a distribution system based on fuzzy-ACO approach. IEEE Trans. Sustain. Energy 6(1), 210–218 (2015)

    Article  Google Scholar 

  32. Von Frisch, K.: Decoding the language of the bee. Science 185(4152), 663–668 (1974)

    Article  Google Scholar 

  33. Xu, H.B., Wang, H.J., Li, C.G.: Fuzzy tabu search method for the clustering problem. In: Proceedings of 2002 International Conference on Machine Learning and Cybernetics, vol. 2, pp. 876–880. IEEE (2002)

    Google Scholar 

  34. Yang, X.S., He, X.: Bat algorithm: literature review and applications. Int. J. Bio-Inspired Comput. 5(3), 141–149 (2013)

    Article  Google Scholar 

  35. Yen, J., Langari, R.: Fuzzy Logic: Intelligence, Control and Information. Prentice Hall (1999)

    Google Scholar 

  36. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  MATH  Google Scholar 

  37. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning, Part I. Inf. Sci. 8, 199–249 (1975)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Graduate Studies, Tijuana Institute of Technology, Tijuana, Mexico

    Leticia Amador-Angulo & Oscar Castillo

Authors
  1. Leticia Amador-Angulo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oscar Castillo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oscar Castillo .

Editor information

Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada

    Marek Reformat

  5. Laboratory of Computational Intelligence and Automation, University of Waterloo, Waterloo, Ontario, Canada

    William Melek

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Amador-Angulo, L., Castillo, O. (2018). Comparative Study of Metrics That Affect in the Performance of the Bee Colony Optimization Algorithm Through Interval Type-2 Fuzzy Logic Systems. In: Melin, P., Castillo, O., Kacprzyk, J., Reformat, M., Melek, W. (eds) Fuzzy Logic in Intelligent System Design. NAFIPS 2017. Advances in Intelligent Systems and Computing, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-319-67137-6_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67137-6_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67136-9

  • Online ISBN: 978-3-319-67137-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation