Skip to main content
SpringerLink
Log in

On the Randomized Firefly Algorithm

  • Chapter
  • First Online:
Cuckoo Search and Firefly Algorithm

Part of the book series: Studies in Computational Intelligence ((SCI,volume 516))

Abstract

The firefly algorithm is a stochastic meta-heuristic that incorporates randomness into a search process. Essentially, the randomness is useful when determining the next point in the search space and therefore has a crucial impact when exploring the new solution. In this chapter, an extensive comparison is made between various probability distributions that can be used for randomizing the firefly algorithm, e.g., Uniform, Gaussian, Lévi flights, Chaotic maps, and the Random sampling in turbulent fractal cloud. In line with this, variously randomized firefly algorithms were developed and extensive experiments conducted on a well-known suite of functions. The results of these experiments show that the efficiency of a distributions largely depends on the type of a problem to be solved.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Co, New York (1979)

    Google Scholar 

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

    Chapter  Google Scholar 

  3. Beekman, M., Sword, G.A., Simpson, S.J.: Biological foundations of swarm intelligence. In: Blum, C., Merkle, D. (eds.) Swarm Intelligence: Introduction and Applications, pp. 3–41. Springer, Berlin (2008)

    Chapter  Google Scholar 

  4. Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. Proceedings of NATO Advanced Workshop on Robots and Biological Systems, pp. 26–30. Tuscany, Italy (1989)

    Google Scholar 

  5. Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 11–32. McGraw Hill, London (1999)

    Google Scholar 

  6. Kennedy, J., Eberhart, R.C.: The particle swarm optimization: social adaptation in information processing. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 379–387. McGraw Hill, London (1999)

    Google Scholar 

  7. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  8. Fister, I., Fister, I. Jr., Brest, J., Žumer, V.: Memetic artificial bee colony algorithm for large-scale global optimization. In: IEEE Congress on Evolutionary Computation, Brisbane, Australia, pp. 3038–3045. IEEE Publications (2012)

    Google Scholar 

  9. Yang, X.-S.: Firefly algorithm. In: Yang, X.-S. (ed.) Nature-Inspired Metaheuristic Algorithms, pp. 79–90. Wiley Online, Library (2008)

    Google Scholar 

  10. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Stochastic Algorithms: Foundations and Applications, pp. 169–178. Springer, Berlin (2009)

    Google Scholar 

  11. Fister, I. Jr., Yang, X.-S., Fister, I., Brest, J.: Memetic firefly algorithm for combinatorial optimization. In: Filipič, B., Šilc, J. (eds.) Bioinspired optimization methods and their applications : proceedings of the Fifth International Conference on Bioinspired Optimization Methods and their Applications—BIOMA 2012, pp. 75–86. Jožef Stefan Institute (2012)

    Google Scholar 

  12. Gandomi, A.H., Yang, X.-S., Talatahari, S., Alavi, A.H.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  13. Fister, I., Yang, X.-S., Brest, J., Fister Jr, I.: Memetic self-adaptive firefly algorithm. In: Yang, X.-S., Xiao, R.Z.C., Gandomi, A.H., Karamanoglu, M. (eds.) Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, pp. 73–102. Elsevier, Amsterdam (2013)

    Chapter  Google Scholar 

  14. Fister, I., Fister Jr., I., Yang, X.-S., Brest, J.: A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation (2013). Available via ScienceDirect. http://www.sciencedirect.com/science/article/pii/S2210650213000461. Cited 03 Jul 2013

  15. Yang, X.-S., Deb, S.: Cuckoo search via Levy flights. In: World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), pp. 210–214. IEEE Publications (2009)

    Google Scholar 

  16. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Cruz, C., Gonzlez, J.R., Krasnogor, N., Pelta, D.A., Terrazas, G. (eds.) Nature Inspired Cooperative Strategies for Optimization (NISCO 2010), vol. 284, pp. 65–74. Springer, Berlin (2010)

    Google Scholar 

  17. Fister Jr, I., Fister, D., Yang, X.-S.: A Hybrid bat algorithm. Electrotech. Rev. 80, 1–7 (2013)

    Google Scholar 

  18. Hoos, H.H., Stützle, T.: Stochastic local search: Foundations and applications. Morgan Kaufmann, San Francisco (2004)

    Google Scholar 

  19. Feldman, D.P.: Chaos and Fractals: An Elementary Introduction. Oxford University Press, Oxford (2012)

    Google Scholar 

  20. Črepinšek, M., Mernik, M., Liu, S.H.: Analysis of exploration and exploitation in evolutionary algorithms by ancestry trees. Int. J. Innovative Comput. Appl. 3, 11–19 (2011)

    Article  Google Scholar 

  21. Hertz, A., Taillard, E., de Werra, D.: Tabu search. In: Aarts, E., Lenstra, J.K. (eds.) Local Search in Combinatorial Optimization, pp. 121–136. Princeton University Press, New Jersey (2003)

    Google Scholar 

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

    Book  MATH  Google Scholar 

  23. Galassi, D., et al.: GNU Scientific Library: Reference Manual, Edn. 1.15. Network Theory Ltd, Bristol (2011)

    Google Scholar 

  24. Jamil, M.: Zepernick: Lévy flights and global optimization. In: Yang, X.-S., Xiao, R.Z.C., Gandomi, A.H., Karamanoglu, M. (eds.) Swarm Intelligence and Bio-Inspired Computation: Theory and Applications, pp. 49–72. Elsevier, Amsterdam (2013)

    Chapter  Google Scholar 

  25. Zhou, Q., Li, L., Chen, Z.-Q., Zhao, J.-X.: Implementation of LT codes based on chaos. Chin. Phys. B 17(10), 3609–3615 (2008)

    Article  Google Scholar 

  26. Elmegreen, B.G.: The initial stellar mass function from random sampling in a turbulent fractal cloud. Astrophys. J. 486, 944–954 (1997)

    Article  Google Scholar 

  27. Long, S.M., Lewis, S., Jean-Louis, L., Ramos, G., Richmond, J., Jakob, E.M.: Firefly flashing and jumping spider predation. Anim. Behav. 83, 81–86 (2012)

    Article  Google Scholar 

  28. Yang, X.-S.: Appendix A: Test Problems in Optimization. In: Yang, X.-S. (ed.) Engineering Optimization, pp. 261–266. John Wiley and Sons, Inc., New York (2010)

    Google Scholar 

  29. Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32, 675–701 (1937)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Iztok Fister, Janez Brest & Iztok Fister Jr.

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

    Xin-She Yang

Authors
  1. Iztok Fister
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin-She Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Janez Brest
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Iztok Fister Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iztok Fister .

Editor information

Editors and Affiliations

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

    Xin-She Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Fister, I., Yang, XS., Brest, J., Fister, I. (2014). On the Randomized Firefly Algorithm. In: Yang, XS. (eds) Cuckoo Search and Firefly Algorithm. Studies in Computational Intelligence, vol 516. Springer, Cham. https://doi.org/10.1007/978-3-319-02141-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02141-6_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02140-9

  • Online ISBN: 978-3-319-02141-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation