Nothing Special   »   [go: up one dir, main page]

Skip to main content

Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any Novelty

  • Conference paper
  • First Online:
Swarm Intelligence (ANTS 2020)

Abstract

In this paper, we carry out a review of the grey wolf, the firefly and the bat algorithms. We identify the concepts involved in these three metaphor-based algorithms and compare them to those proposed in the context of particle swarm optimization. We provide compelling evidence that the grey wolf, the firefly, and the bat algorithms are not novel, but a reiteration of ideas introduced first for particle swarm optimization and reintroduced years later using new natural metaphors. These three algorithms can therefore be added to the growing list of metaphor-based algorithms—to which already belong algorithms such as harmony search and intelligent water drops—that are nothing else than repetitions of old ideas hidden by the usage of new terminology.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Grey Wolf Optimizer  [14]: 3656 citations; Firefly Algorithm  [28]: 3018 citations; and Bat Algorithm  [29]: 3549 citations. Source: Google Scholar. Retrieved: July 10, 2020.

  2. 2.

    Although search is not an activity in the hunting phases of wolves, the authors explain it as “the divergence among wolves during hunting in order to find a fitter prey” [14, p. 50].

  3. 3.

    Note that in the following we will use the shorter notation \(\varphi ^{\textit{\textbf{w}},\textit{\textbf{m}}}_t\) when the meaning is clear from the context.

  4. 4.

    In this paper, we consider minimization problems; the obvious adaptation should be made in case of maximization problems.

  5. 5.

    Due to the constraint that both conditions have to be met, it may be the case that \(\textit{\textbf{z}}^{i}_{t}\) is rejected even when its quality is higher than that of \(\textit{\textbf{g}}_{t}\).

  6. 6.

    Note that, although in this paper we compared BA with PSO and SA, BA could also be interpreted as a variant of differential evolution (DE) [25]. This is because the probability \(\rho ^i_t\) and the \(\texttt {Accept}\) criterion in BA are used in the same way as the mutation probability and the acceptance between donor and trial vectors in DE [18].

References

  1. Arumugam, M.S., Murthy, G.R., Rao, M., Loo, C.X.: A novel effective particle swarm optimization like algorithm via extrapolation technique. In: International Conference on Intelligent and Advanced Systems, pp. 516–521. IEEE (2007)

    Google Scholar 

  2. Camacho-Villalón, C.L., Dorigo, M., Stützle, T.: Why the Intelligent Water Drops Cannot Be Considered as a Novel Algorithm. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Reina, A., Trianni, V. (eds.) ANTS 2018. LNCS, vol. 11172, pp. 302–314. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00533-7_24

    Chapter  Google Scholar 

  3. Camacho-Villalón, C.L., Dorigo, M., Stützle, T.: The intelligent water drops algorithm: why it cannot be considered a novel algorithm. Swarm Intell. 13, 173–192 (2019). https://doi.org/10.1007/s11721-019-00165-y

    Article  Google Scholar 

  4. Campelo, F.: Evolutionary computation bestiary. https://github.com/fcampelo/EC-Bestiary (2017). Accessed 22 Jan 2018

  5. Clerc, M.: Standard particle swarm optimisation from 2006 to 2011. Open archive HAL hal-00764996, HAL (2011)

    Google Scholar 

  6. Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)

    Article  Google Scholar 

  7. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)

    Google Scholar 

  8. Kennedy, J.: Bare bones particle swarms. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003 (Cat. No. 03EX706), pp. 80–87. IEEE (2003)

    Google Scholar 

  9. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995 International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)

    Google Scholar 

  10. Kirkpatrick, S.: Optimization by simulated annealing: quantitative studies. J. Stat. Phys. 34(5–6), 975–986 (1984). https://doi.org/10.1007/BF01009452

    Article  MathSciNet  Google Scholar 

  11. Lones, M.A.: Metaheuristics in nature-inspired algorithms. In: Igel, C., Arnold, D.V. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2014. pp. 1419–1422. ACM Press, New York (2014)

    Google Scholar 

  12. Melvin, G., Dodd, T.J., Groß, R.: Why ‘GSA: a gravitational search algorithm’ is not genuinely based on the law of gravity. Natural Comput. 11(4), 719–720 (2012). https://doi.org/10.1007/s11047-012-9322-0

    Article  MathSciNet  MATH  Google Scholar 

  13. Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8(3), 204–210 (2004)

    Article  Google Scholar 

  14. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014)

    Article  Google Scholar 

  15. Peña, J.: Simple dynamic particle swarms without velocity. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 144–154. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87527-7_13

    Chapter  Google Scholar 

  16. Peña, J.: Theoretical and empirical study of particle swarms with additive stochasticity and different recombination operators. In: Ryan, C. (ed.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2008, pp. 95–102. ACM Press, New York (2008)

    Google Scholar 

  17. Piotrowski, A.P., Napiorkowski, J.J., Rowinski, P.M.: How novel is the "novel" black hole optimization approach? Inf. Sci. 267, 191–200 (2014)

    Article  Google Scholar 

  18. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution. NCS. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-31306-0

    Book  MATH  Google Scholar 

  19. Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans. Evol. Comput. 8(3), 240–255 (2004)

    Article  Google Scholar 

  20. Rechenberg, I.: Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, Stuttgart, Germany (1973)

    Google Scholar 

  21. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Simpson, P.K., Haines, K., Zurada, J., Fogel, D. (eds.) Proceedings of the 1998 IEEE International Conference on Evolutionary Computation, ICEC 1998, pp. 69–73. IEEE Press, Piscataway (1998)

    Google Scholar 

  22. Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Proceedings of the 2009 Congress on Evolutionary Computation (CEC 2009), pp. 1945–1950. IEEE Press, Piscataway (2009)

    Google Scholar 

  23. Sörensen, K.: Metaheuristics–the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015). https://doi.org/10.1111/itor.12001

    Article  MathSciNet  MATH  Google Scholar 

  24. Sörensen, K., Arnold, F., Palhazi Cuervo, D.: A critical analysis of the “improved Clarke and wright savings algorithm”. Int. Trans. Oper. Res. 26(1), 54–63 (2019)

    Article  MathSciNet  Google Scholar 

  25. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997). https://doi.org/10.1023/A:1008202821328

    Article  MathSciNet  MATH  Google Scholar 

  26. Weyland, D.: A rigorous analysis of the harmony search algorithm: how the research community can be misled by a “novel” methodology. Int. J. Appl. Metaheuristic Comput. 12(2), 50–60 (2010)

    Article  Google Scholar 

  27. Weyland, D.: A critical analysis of the harmony search algorithm: how not to solve Sudoku. Oper. Res. Pers. 2, 97–105 (2015)

    MathSciNet  Google Scholar 

  28. Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04944-6_14

    Chapter  Google Scholar 

  29. Yang, X.S.: A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010). In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol. 284, pp. 65–74. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12538-6_6

    Chapter  Google Scholar 

  30. Zambrano-Bigiarin, M., Clerc, M., Rojas, R.: Standard particle swarm optimisation 2011 at cec-2013: a baseline for future pso improvements. In: Proceedings of the 2013 Congress on Evolutionary Computation (CEC 2013), pp. 2337–2344. IEEE Press, Piscataway (2013)

    Google Scholar 

Download references

Acknowledgments

Christian Leonardo Camacho Villalón, Thomas Stützle and Marco Dorigo acknowledge support from the Belgian F.R.S.-FNRS, of which they are, respectively, research fellow and research directors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christian Leonardo Camacho Villalón .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Camacho Villalón, C.L., Stützle, T., Dorigo, M. (2020). Grey Wolf, Firefly and Bat Algorithms: Three Widespread Algorithms that Do Not Contain Any Novelty. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60376-2_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60375-5

  • Online ISBN: 978-3-030-60376-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics