Skip to main content
Log in

The raven roosting optimisation algorithm

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

A significant stream of literature which draws inspiration from the foraging activities of various organisms to design optimisation algorithms has emerged over the past decade. The success of these algorithms across a wide variety of application domains has spurred interest in the examination of the foraging behaviours of other organisms to develop novel and powerful, optimisation algorithms. A variety of animals, including some species of birds and bats, engage in social roosting whereby large numbers of conspecifics gather together to roost, either overnight or for longer periods. It has been claimed that these roosts can serve as information centres to spread knowledge concerning the location of food resources in the environment. In this paper we look at the social roosting and foraging behaviour of one species of bird, the common raven, and take inspiration from this to design a novel optimisation algorithm which we call the raven roosting optimisation algorithm. The utility of the algorithm is assessed on a series of benchmark problems and the results are found to be competitive. We also provide a novel taxonomy which classifies foraging-inspired optimisation algorithms based on the underlying social communication mechanism embedded in the algorithms.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  • Anderson J (1991) Foraging behavior of the American white pelican (Pelecanus erythrorhyncos) in western Nevada. Colonial Waterbirds 14:166–172

    Article  Google Scholar 

  • Benoit-Bird K, Au W (2009) Cooperative prey herding by the pelagic dolphin Stenella longirostris. J Acoust Soc Am 125(1):125–137

    Article  Google Scholar 

  • Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, Oxford

    MATH  Google Scholar 

  • Bradbury J, Vehrencamp S (2011) Principles of animal communication, 2nd edn. Sinauer Associates, Sunderland

    Google Scholar 

  • Chong C, Low M, Sivakumar A, Gay K (2006) A bee colony optimization algorithm to job shop scheduling. In: Proceedings of the 2006 winter simulation conference (WinterSim 2006). IEEE Press, New Jersey, pp 1954–1961

  • Dall S (2002) Can information sharing explain recruitment to food from communal roosts? Behav Ecol 13(1):42–51

    Article  MathSciNet  Google Scholar 

  • Davies N, Krebs J, West S (2012) An introduction to behavioural ecology, 4th edn. Wiley-Blackwell, Chichester

    Google Scholar 

  • Deygout C, Gault A, Duriez O, Sarrazin F, Bessa-Gomes C (2010) Impact of food predictability on social facilitation by foraging scavengers. Behav Ecol 21(6):1131–1139

    Article  Google Scholar 

  • Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di Milano

  • Dorigo M, DiCaro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of IEEE congress on evolutionary computation (CEC 1999). IEEE Press, pp 1470–1477

  • Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B: Cybern 26(1):29–41

    Article  Google Scholar 

  • Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, Cambridge

    Book  MATH  Google Scholar 

  • Emlen J (1966) The role of time and energy in food preference. Am Nat 100(916):611–617

    Article  Google Scholar 

  • Fleming T (1982) Foraging strategies of plant-visiting bats. In: Kunz T (ed) Ecology of bats. Plenum Press, New York, pp 287–325

    Chapter  Google Scholar 

  • Franklin E, Franks N (2012) Individual and social learning in tandem-running recruitment by ants. Anim Behav 84:361–368

    Article  Google Scholar 

  • Ganesan T, Vasant P, Elamvazuthy I (2012) A hybrid PSO approach for solving non-convex optimization problems. Arch Control Sci 22(1):87–105

    MATH  Google Scholar 

  • Giraldeau LA, Caraco T (2000) Social foraging theory. Princeton University Press, New Jersey

    Google Scholar 

  • Grüter C, Segers F, Ratnieks F (2013) Social learning strategies in honeybee foragers: do the costs of using private information affect the use of social information? Anim Behav. doi: 10.1016/j.anbehav.2013.03.041

  • Grüter C, Leadbeater E (2014) Insights from insects about adaptive social information use. Trends Ecol Evol 29(3):177–184

    Article  Google Scholar 

  • Leadbeater E, Florent C (2014) Foraging bumblebees do not rate social information above personal experience. Behav Ecol Sociobiol 68:1145–1150

    Article  Google Scholar 

  • Le Dinh L, Ngoc V, Vasant P (2013) Artificial bee colony algorithm for solving optimal power flow problem. Sci World J 2013:159040

    Google Scholar 

  • Lonnstedt O, Ferrari M, Chivers D (2014) Lionfish predators use flared fin displays to initiate cooperative hunting. Biol Lett 10:20140281

  • Kennedy J, Eberhart R (1995) Particle swarm optimization, In: Proceedings of the IEEE international conference on neural networks. IEEE Press, pp 1942–1948

  • Kennedy J, Eberhart R, Shi T (2001) Swarm intelligence. Morgan Kaufman, San Mateo

  • Marzluff J, Heinrich B, Marzluff C (1996) Raven roosts are mobile information centres. Anim Behav 51:89–103

    Article  Google Scholar 

  • Marzluff J, Heinrich B (2001) Raven roosts are still information centres. Anim Behav 61:F14–F15

    Google Scholar 

  • Nakrani S, Tovey C (2004) On honey bees and dynamic server allocation in internet hosting centres. Adaptive Behav 12(3–4):223–240

    Article  Google Scholar 

  • Passino K (2000) Distributed Optimization and Control Using Only a Germ of Intelligence. In: Proceedings of the IEEE international symposium on intelligent control. IEEE Press, pp 5–13

  • Passino K (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67

    Article  MathSciNet  Google Scholar 

  • Pham D, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2006) The bees algorithm—a novel tool for complex optimisation problems. In: Proceedings of international production machines and systems (IPROMS 2006). Elsevier, UK, pp 454–459

  • Seeley T (1995) The wisdom of the hive. Harvard University Press, Cambridge

    Google Scholar 

  • Serfass T (1995) Cooperative forgaging by North American river otters Lutra canadensis. Can Field Nat 4:458–459

    Google Scholar 

  • Stahler D, Heinrich B, Smith D (2002) Common ravens, Corvus corax, preferentially associate with grey wolves, Canis lupus, as a foraging strategy in winter. Anim Behav 64:283–290

    Article  Google Scholar 

  • Stephens D, Krebs J (1986) Foraging theory. Princeton University Press, New Jersey

    Google Scholar 

  • von Frisch K (1967) The dance language and orientation of bees. Harvard University Press, Cambridge

    Google Scholar 

  • Viswanathan G, da Luz M, Raposo E, Stanley E (2011) The physics of foraging: an introduction to random searches and biological encounters. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Ward P, Zahavi A (1973) The importance of certain assemblages of birds as ‘information centres’ for food finding. Ibis 115:517–534

    Article  Google Scholar 

  • Wilkinson G (1992) Information transfer at evening bat colonies. Anim Behav 44(3):501–518

    Article  Google Scholar 

  • Wray M, Klein B, Seeley T (2012) Honey bees use social information in waggle dances more fully when foraging errors are more costly. Behav Ecol 23(1):125–131

  • Wright J, Stone R, Brown N (2003) Communal roosts as structured information centres in the raven, Corvus corax. J Anim Ecol 72:1003–1014

    Article  Google Scholar 

  • Yang XS (2005) Engineering optimization via nature-inspired virtual bee algorithms. In: Mira J, Álvarez J (eds) Artificial intelligence and knowledge engineering applications: a bioinspired approach. Springer, Berlin, pp 317–323

    Chapter  Google Scholar 

  • Zahavi A (1971) The function of pre-roost gatherings and communal roosts. Ibis 113:106–109

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the contribution of the anonymous reviewers to the improvement of this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anthony Brabazon.

Additional information

Communicated by V. Loia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Brabazon, A., Cui, W. & O’Neill, M. The raven roosting optimisation algorithm. Soft Comput 20, 525–545 (2016). https://doi.org/10.1007/s00500-014-1520-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-014-1520-5

Keywords

Navigation