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

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Swarm Intelligence (ANTS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12421))

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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.

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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].

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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.

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Authors and Affiliations

  1. IRIDIA, Université Libre de Bruxelles, Brussels, Belgium

    Christian Leonardo Camacho Villalón, Thomas Stützle & Marco Dorigo

Authors
  1. Christian Leonardo Camacho Villalón
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  2. Thomas Stützle
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  3. Marco Dorigo
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Corresponding author

Correspondence to Christian Leonardo Camacho Villalón .

Editor information

Editors and Affiliations

  1. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Thomas Stützle

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Maria J. Blesa

  4. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  5. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  6. Université Libre de Bruxelles, Brussels, Belgium

    Mary Katherine Heinrich

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

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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

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

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  • Online ISBN: 978-3-030-60376-2

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