Skip to main content

Creativity and Autonomy in Swarm Intelligence Systems


This work introduces two swarm intelligence algorithms—one mimicking the behaviour of one species of ants (Leptothorax acervorum) foraging (a ‘stochastic diffusion search’, SDS) and the other algorithm mimicking the behaviour of birds flocking (a ‘particle swarm optimiser’, PSO)—and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploiting an artistic tension between the local behaviour of the ‘birds flocking’—as they seek to follow the input sketch—and the global behaviour of the ‘ants foraging’—as they seek to encourage the flock to explore novel regions of the canvas. The paper concludes by exploring the putatve ‘creativity’ of this hybrid swarm system in the philosophical light of the ‘rhizome’ and Deleauze’s well-known ‘Orchid and Wasp’ metaphor.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. For this work, we consider a ‘drawing’ to be a representation of a target image, built up from an arrangement of lines which define its form; for the purposes of this work, a drawing where all aspects of the original image are obscured is considered a poor ‘drawing’ of the target (albeit it may [or may-not] be an aesthetically pleasing object in its own right); a ‘creative’ drawing of the target is a drawing that differs noticeably from the original, whilst maintaining good correspondence [hi-fidelity] with at least some aspects of the original, such that the target image is still ‘recognisable’ in the resultant drawing.

  2. Although in principle, both functions (exploration and exploitation; local and global search of the conceptual space) could be carried out by either algorithm on its own, the basic SDS mechanism is not the best local optimiser and similarly a ‘standard’ PSO is not the best global optimiser, hence the motivation for exploring the properties of their hybridisation; promising early results of which have been reported elsewhere [1].


  1. al-Rifaie MM, Bishop M, Blackwell T. An investigation into the merger of stochastic diffusion search and particle swarm optimisation. In: GECCO ’11: Proceedings of the 2011 GECCO conference companion on genetic and evolutionary computation. New York, NY: ACM; 2011. p. 37–44.

  2. Aupetit S, Bordeau V, Monmarche N, Slimane M, Venturini G. Interactive evolution of ant paintings. In: The 2003 congress on evolutionary computation, 2003 (CEC’03), vol 2. 2004. p. 1376–83.

  3. Basch LGS. Nations unbound: transnational projects, postcolonial predicaments and deterritorialized nation-states, 1st ed. London: Routledge; 1993.

  4. Bayazit OB, Lien JM, Amato NM. Roadmap-based flocking for complex environments. In: PG ’02: Proceedings of the 10th Pacific conference on computer graphics and applications. Washington, DC: IEEE Computer Society; 2002. p. 104.

  5. Bishop J. Stochastic searching networks. In: Proceedings of 1st IEE conference on artificial neural networks, London, UK; 1989. p. 329–31.

  6. Boden M. Creativity in a nutshell. Think. 2007;5(15):83–96.

    Article  Google Scholar 

  7. Boden M. Creativity and art: three roads to surprise. Oxford: Oxford University Press; 2010.

    Google Scholar 

  8. Bonabeau E, Dorigo M, Theraulaz G. Inspiration for optimization from social insect behaviour. Nature. 2000;406:3942.

    Article  Google Scholar 

  9. Borgia G. Complex male display and female choice in the spotted bowerbird: specialized functions for different bower decorations. Anim Behav. 1995; 49:1291–301.

    Article  Google Scholar 

  10. Bown O. Generative and adaptive creativity. In: McCormack J, d’Inverno M, editors. In computers and creativity. Berlin: Springer; 2011.

    Google Scholar 

  11. Clark A. Natural-born cyborgs: minds, technologies, and the future of human intelligence. New York: Oxford University Press; 2003.

    Google Scholar 

  12. Deleuze G, Guattari F, Massumi B. A thousand plateaus. Minneapolis: University of Minnesota Press; 2004.

    Google Scholar 

  13. Dorin A, Korb K. Creativity refined. In: McCormack J, d’Inverno M, editors. In computers and creativity. Berlin: Springer; 2011.

    Google Scholar 

  14. Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium on micro machine and human science, vol 43. New York, NY: IEEE; 1995.

  15. Etzioni A, Ben-Barak A, Peron S, Durandy A. Ataxia-telangiectasia in twins presenting as autosomal recessive hyper-immunoglobulin m syndrome. IMAJ-RAMAT GAN. 2007; 9(5):406.

    Google Scholar 

  16. Galanter P. Computational aesthetic evaluation: past and future. In: McCormack J, d’Inverno M, editors. In computers and creativity. Berlin: Springer; 2011.

    Google Scholar 

  17. Greenfield G. Evolutionary methods for ant colony paintings. Appl Evol Comput Proc. 2005;3449:478–87.

    Article  Google Scholar 

  18. Heppner F, Grenander U. A stochastic nonlinear model for coordinated bird flocks. Washington, DC: American Association for the Advancement of Science; 1990.

    Google Scholar 

  19. Holldobler B, Wilson EO. The ants. Berlin: Springer; 1990.

    Google Scholar 

  20. Janson CH. Experimental evidence for spatial memory in foraging wild capuchin monkeys, cebus apella. Anim Behav. 1998; 55:1229–43.

    PubMed  Article  Google Scholar 

  21. Kennedy J, Eberhart RC. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, vol IV. Piscataway, NJ: IEEE Service Center; 1995. p. 1942–8.

  22. Kennedy JF, Eberhart RC, Shi Y. Swarm intelligence. San Francisco, London: Morgan Kaufmann; 2001.

    Google Scholar 

  23. Levy S. Artificial life: a report from the frontier where computers meet biology. New York: Vintage Books; 1993.

    Google Scholar 

  24. Mataric M. Interaction and intelligent behavior. Ph.D. thesis, Department of Electrical, Electronics and Computer Engineering, MIT, USA; 1994.

  25. McCorduck P. Aaron’s code: meta-art, artificial intelligence, and the work of Harold Cohen. New York: WH Freeman; 1991.

    Google Scholar 

  26. de Meyer K, Bishop JM, Nasuto SJ. Stochastic diffusion: using recruitment for search. In: McOwan P, Dautenhahn K, Nehaniv CL, editors. Evolvability and interaction: evolutionary substrates of communication, signalling, and perception in the dynamics of social complexity. Technical Report 393. 2003. p. 60–5.

  27. de Meyer K, Nasuto S, Bishop J. Stochastic diffusion optimisation: the application of partial function evaluation and stochastic recruitment in swarm intelligence optimisation. In Abraham A, Grosam C, Ramos V, editors. Swarm intelligence and data mining, vol 2, Chapter 12. Springer; 2006.

  28. Moglich M, Maschwitz U, Holldobler B. Tandem calling: a new kind of signal in ant communication. Science. 1974;186(4168):1046–7.

    PubMed  Article  CAS  Google Scholar 

  29. Monmarche N, Aupetit S, Bordeau V, Slimane M, Venturini G. Interactive evolution of ant paintings. In: McKay B, et al., editors. 2003 Congress on evolutionary computation, vol 2. IEEE Press; 2003. p. 1376–83.

  30. Moura L, Ramos V. Swarm paintings—nonhuman art. ARCHITOPIA book, art, architecture and science. 2007. p. 5–24.

  31. Myatt DR, Bishop JM, Nasuto SJ. Minimum stable convergence criteria for stochastic diffusion search. Electron Lett. 2004; 40(2):112–3.

    Article  Google Scholar 

  32. Nagel T. What is it like to be a bat? Philos Rev. 1974; 83(4):435–50.

    Article  Google Scholar 

  33. Nasuto SJ. Resource allocation analysis of the stochastic diffusion search. Ph.D. thesis, University of Reading, Reading, UK; 1999.

  34. Nasuto SJ, Bishop JM. Convergence analysis of stochastic diffusion search. Parallel Algorithms Appl. 1999; 14(2):89–107.

    Google Scholar 

  35. Nasuto SJ, Bishop JM, Lauria S. Time complexity of stochastic diffusion search. In: Neural computation (NC98); 1998.

  36. O’Sullivan S. et al. Art encounters Deleuze and Guattari: thought beyond representation. Basingstoke: Palgrave Macmillan; 2006.

    Google Scholar 

  37. Restany P. Hundertwasser: the painter-king with the five skins: the power of art. Los Angeles, CA: Taschen America Llc; 2001.

    Google Scholar 

  38. Reynolds CW. Flocks, herds, and schools: a distributed behavioral model. Comput Graph. 1987; 21(4):25–34.

    Article  Google Scholar 

  39. Richards ME, Caines S. Unconventional encounters. Perform Res J. 2012 (submitted).

  40. Rothenberg A, Hausman C. The creativity question. Durham, NC: Duke University Press Books; 1976.

    Google Scholar 

  41. Saxe JG, Lathen D, Chief B. The blind man and the elephant. The Poems of John Godfrey Saxe; 1882.

  42. Schermerhorn P, Scheutz M. The impact of communication and memory in hive-based foraging agents. In: IEEE symposium on artificial life, 2009 (ALife’09). 2009. p. 29–36.

  43. Shi Y, Eberhart RC. Parameter selection in particle swarm optimization. In: Lecture notes in computer science. p. 591–600.

  44. Sims K. Artificial evolution for computer graphics. Comput Graph. 1991; 25(4):319–28.

    Article  Google Scholar 

  45. Sims K. Evolving 3d morphology and behavior by competition. Artif Life. 1994; 1(4):353–72.

    Article  Google Scholar 

  46. Sternberg R. The nature of creativity: contemporary psychological perspectives. New York: Cambridge University Press; 1988.

    Google Scholar 

  47. Taylor C. 4 Various approaches to and definitions of creativity. The nature of creativity: Contemporary psychological perspectives. 1988. p. 99.

  48. Urbano P. Playing in the pheromone playground: experiences in swarm painting. Appl Evol Comput. 2005; 3449:527–32.

    Google Scholar 

  49. Urbano P. Consensual paintings. Appl Evol Comput. 2006; 3907:622–32.

  50. Watanabe S. Pigeons can discriminate “good” and “bad” paintings by children. Anim Cogn. 2009; 13(1):75–85.

    Google Scholar 

  51. Weesatchanam AM. Are paintings by elephants really art? The Elephant Art Gallery. 2006.

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Mohammad Majid al-Rifaie.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

al-Rifaie, M.M., Bishop, J.M. & Caines, S. Creativity and Autonomy in Swarm Intelligence Systems. Cogn Comput 4, 320–331 (2012).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • PSO
  • SDS
  • Autonomy
  • Swarm intelligence
  • Computational creativity