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Parameterless Bat Algorithm and Its Performance Study

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Book cover Nature-Inspired Computation in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 637))

Abstract

A parameter-free or parameterless bat algorithm is a new variant of the bat algorithm which was recently introduced. Characteristic of this algorithm is that user does not need to specify the control parameters when running this algorithm. Thus, this bat algorithm variant can have wide usability in solving real-world optimization problems. In this chapter, a preliminary study of the proposed parameterless bat algorithm is presented.

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References

  1. Blum, C., Li, X.: Swarm intelligence in optimization. In: Blum, C., Merkle, D. (eds.) Swarm Intelligence: Introduction and Applications, pp. 43–86. Springer, Berlin (2008)

    Google Scholar 

  2. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

  3. Derrac, J., Garca, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)

    Google Scholar 

  4. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin (2003)

    Google Scholar 

  5. Fister, I., Yang, X.-S., Brest, J., Fister Jr., I.: Modified firefly algorithm using quaternion representation. Expert Syst. Appl. 40(18), 7220–7230 (2013)

    Google Scholar 

  6. Fister Jr., I., Fister, I., Yang, X.-S.: Towards the development of a parameter-free bat algorithm. In: StuCoSReC: Proceedings of the 2015 2nd Student Computer Science Research Conference, pp. 31–34 (2015)

    Google Scholar 

  7. Fister Jr., I., Yang, X.-S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization. Elektrotehniški vestnik 80(3), 116–122 (2013)

    Google Scholar 

  8. Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11, 86–92 (1940)

    Article  MathSciNet  MATH  Google Scholar 

  9. Holtschulte, N., Moses, M.: Should every man be an island. In: GECCO 2013 Proceedings, 8 pp. (2013)

    Google Scholar 

  10. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  11. Lobo, F.G., Goldberg, D.E.: An overview of the parameter-less genetic algorithm. In: Proceedings of the 7th Joint Conference on Information Sciences (Invited paper), pp. 20–23 (2003)

    Google Scholar 

  12. Nemenyi, P.B.: Distribution-free multiple comparisons. Ph.D. thesis, Princeton University (1963)

    Google Scholar 

  13. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer (2010)

    Google Scholar 

  14. Yang, X.-S.: Nature-Inspired Optimization Algorithms. Elsevier (2014)

    Google Scholar 

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

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000, Maribor, Slovenia

    Iztok Fister Jr., Uroš Mlakar & Iztok Fister

  2. School of Science and Technology, Middlesex University, London, NW4 4BT, UK

    Xin-She Yang

Authors
  1. Iztok Fister Jr.
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  2. Uroš Mlakar
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  3. Xin-She Yang
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  4. Iztok Fister
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Corresponding author

Correspondence to Iztok Fister Jr. .

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Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

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© 2016 Springer International Publishing Switzerland

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Fister, I., Mlakar, U., Yang, XS., Fister, I. (2016). Parameterless Bat Algorithm and Its Performance Study. In: Yang, XS. (eds) Nature-Inspired Computation in Engineering. Studies in Computational Intelligence, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-319-30235-5_13

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  • DOI: https://doi.org/10.1007/978-3-319-30235-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30233-1

  • Online ISBN: 978-3-319-30235-5

  • eBook Packages: EngineeringEngineering (R0)

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