Soft Computing

, Volume 20, Issue 2, pp 525–545 | Cite as

The raven roosting optimisation algorithm

Methodologies and Application

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.

Keywords

Social foraging Social roosting Raven roosting Information centre Optimisation 

References

  1. Anderson J (1991) Foraging behavior of the American white pelican (Pelecanus erythrorhyncos) in western Nevada. Colonial Waterbirds 14:166–172CrossRefGoogle Scholar
  2. Benoit-Bird K, Au W (2009) Cooperative prey herding by the pelagic dolphin Stenella longirostris. J Acoust Soc Am 125(1):125–137CrossRefGoogle Scholar
  3. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, OxfordMATHGoogle Scholar
  4. Bradbury J, Vehrencamp S (2011) Principles of animal communication, 2nd edn. Sinauer Associates, SunderlandGoogle Scholar
  5. 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 1954–1961Google Scholar
  6. Dall S (2002) Can information sharing explain recruitment to food from communal roosts? Behav Ecol 13(1):42–51MathSciNetCrossRefGoogle Scholar
  7. Davies N, Krebs J, West S (2012) An introduction to behavioural ecology, 4th edn. Wiley-Blackwell, ChichesterGoogle Scholar
  8. 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):1131–1139CrossRefGoogle Scholar
  9. Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. thesis, Politecnico di MilanoGoogle Scholar
  10. 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 1470–1477Google Scholar
  11. 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):29–41CrossRefGoogle Scholar
  12. Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, CambridgeMATHCrossRefGoogle Scholar
  13. Emlen J (1966) The role of time and energy in food preference. Am Nat 100(916):611–617CrossRefGoogle Scholar
  14. Fleming T (1982) Foraging strategies of plant-visiting bats. In: Kunz T (ed) Ecology of bats. Plenum Press, New York, pp 287–325CrossRefGoogle Scholar
  15. Franklin E, Franks N (2012) Individual and social learning in tandem-running recruitment by ants. Anim Behav 84:361–368CrossRefGoogle Scholar
  16. Ganesan T, Vasant P, Elamvazuthy I (2012) A hybrid PSO approach for solving non-convex optimization problems. Arch Control Sci 22(1):87–105MATHGoogle Scholar
  17. Giraldeau LA, Caraco T (2000) Social foraging theory. Princeton University Press, New JerseyGoogle Scholar
  18. 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
  19. Grüter C, Leadbeater E (2014) Insights from insects about adaptive social information use. Trends Ecol Evol 29(3):177–184CrossRefGoogle Scholar
  20. Leadbeater E, Florent C (2014) Foraging bumblebees do not rate social information above personal experience. Behav Ecol Sociobiol 68:1145–1150CrossRefGoogle Scholar
  21. Le Dinh L, Ngoc V, Vasant P (2013) Artificial bee colony algorithm for solving optimal power flow problem. Sci World J 2013:159040Google Scholar
  22. Lonnstedt O, Ferrari M, Chivers D (2014) Lionfish predators use flared fin displays to initiate cooperative hunting. Biol Lett 10:20140281Google Scholar
  23. Kennedy J, Eberhart R (1995) Particle swarm optimization, In: Proceedings of the IEEE international conference on neural networks. IEEE Press, pp 1942–1948Google Scholar
  24. Kennedy J, Eberhart R, Shi T (2001) Swarm intelligence. Morgan Kaufman, San MateoGoogle Scholar
  25. Marzluff J, Heinrich B, Marzluff C (1996) Raven roosts are mobile information centres. Anim Behav 51:89–103CrossRefGoogle Scholar
  26. Marzluff J, Heinrich B (2001) Raven roosts are still information centres. Anim Behav 61:F14–F15Google Scholar
  27. Nakrani S, Tovey C (2004) On honey bees and dynamic server allocation in internet hosting centres. Adaptive Behav 12(3–4):223–240CrossRefGoogle Scholar
  28. 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 5–13Google Scholar
  29. Passino K (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22(3):52–67MathSciNetCrossRefGoogle Scholar
  30. 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 454–459Google Scholar
  31. Seeley T (1995) The wisdom of the hive. Harvard University Press, CambridgeGoogle Scholar
  32. Serfass T (1995) Cooperative forgaging by North American river otters Lutra canadensis. Can Field Nat 4:458–459Google Scholar
  33. 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 64:283–290CrossRefGoogle Scholar
  34. Stephens D, Krebs J (1986) Foraging theory. Princeton University Press, New JerseyGoogle Scholar
  35. von Frisch K (1967) The dance language and orientation of bees. Harvard University Press, CambridgeGoogle Scholar
  36. 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, CambridgeCrossRefGoogle Scholar
  37. Ward P, Zahavi A (1973) The importance of certain assemblages of birds as ‘information centres’ for food finding. Ibis 115:517–534CrossRefGoogle Scholar
  38. Wilkinson G (1992) Information transfer at evening bat colonies. Anim Behav 44(3):501–518CrossRefGoogle Scholar
  39. 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):125–131Google Scholar
  40. Wright J, Stone R, Brown N (2003) Communal roosts as structured information centres in the raven, Corvus corax. J Anim Ecol 72:1003–1014CrossRefGoogle Scholar
  41. 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 317–323CrossRefGoogle Scholar
  42. Zahavi A (1971) The function of pre-roost gatherings and communal roosts. Ibis 113:106–109CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Complex Adaptive Systems Laboratory and School of BusinessUniversity College DublinDublinIreland

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