Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Inspiration for optimization from social insect behaviour

Abstract

Research in social insect behaviour has provided computer scientists with powerful methods for designing distributed control and optimization algorithms. These techniques are being applied successfully to a variety of scientific and engineering problems. In addition to achieving good performance on a wide spectrum of ‘static’ problems, such techniques tend to exhibit a high degree of flexibility and robustness in a dynamic environment.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Typical result of a comparison of AntNet, an Ant Colony Routing algorithm, with other widespread routing algorithms for packet-switched networks (see ref.41 for an overview of communications networks).

References

  1. 1

    Deneubourg, J. -L. & Goss, S. Collective patterns and decision making. Ethol. Ecol. Evol. 1, 295–311 (1989).

    Article  Google Scholar 

  2. 2

    Goss, S., Aron, S., Deneubourg, J. -L. & Pasteels, J. M. Self-organized shortcuts in the Argentine ant. Naturwissenschaften 76, 579–581 ( 1989).

    ADS  Article  Google Scholar 

  3. 3

    Bonabeau, E., Dorigo, M. & Theraulaz, G. Swarm Intelligence: From Natural to Artificial Systems (Oxford Univ. Press, New York, 1999).

    MATH  Google Scholar 

  4. 4

    Dorigo, M., Maniezzo, V. & Colorni, A. The ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B 6, 29–41 (1996).

    Article  Google Scholar 

  5. 5

    Dorigo, M. & Gambardella, L. M. Ant colonies for the traveling salesman problem. BioSystems 43, 73– 81 (1997).

    CAS  Article  Google Scholar 

  6. 6

    Schoonderwoerd, R., Holland, O., Bruten, J. & Rothkrantz, L. Ant-based load balancing in telecommunications networks. Adapt. Behav. 5, 169–207 (1997).

    Article  Google Scholar 

  7. 7

    Heusse, M., Guérin, S., Snyers, D. & Kuntz, P. Adaptive agent-driven routing and load balancing in communication networks. Adv. Compl. Syst. 1, 237– 254 (1998).

    Article  Google Scholar 

  8. 8

    Di Caro, G. & Dorigo, M. AntNet: Distributed stigmergetic control for communications networks. J. Artif. Intell. Res. 9, 317–365 (1998).

    Article  Google Scholar 

  9. 9

    Lumer, E. & Faieta, B. in Proc. 3rd Intl Conf. Simulation of Adaptive Behavior: From Animals to Animats 3 (eds Cliff, D., Husbands, P., Meyer, J. -A. & Wilson, S. W.) 501–508 (MIT Press, Cambridge, MA, 1994).

    Google Scholar 

  10. 10

    Kuntz, P., Snyers, D. & Layzell, P. A stochastic heuristic for visualizing graph clusters in a bi-dimensional space prior to partitioning. J. Heuristics 5, 327–351 ( 1999).

    Article  Google Scholar 

  11. 11

    Chen, K. A simple learning algorithm for the traveling salesman problem. Phys. Rev. E 55, 7809–7812 (1997).

    ADS  CAS  Article  Google Scholar 

  12. 12

    Baluja, S. & Caruana, R. in Proc. 12th Intl Conf. Machine Learning (eds Prieditis, A. & Russell, S.) 38–46 (Morgan Kaufmann, Palo Alto, 1995).

    Google Scholar 

  13. 13

    Ward, M. There's an ant in my phone. New Sci. 2118, 32–35 (1998).

    Google Scholar 

  14. 14

    Robinson, G. E. Regulation of division of labour in insect societies. Annu. Rev. Entomol. 37, 637–665 ( 1992).

    CAS  Article  Google Scholar 

  15. 15

    Sobkowski, A. et al. in Bio-Computation and Emergent Computing (eds Lundh, D., Olsson B. & Narayanan, A.) 36–45 (World Scientific, Singapore, 1997).

    Google Scholar 

  16. 16

    Franks, N. R. & Sendova-Franks, A. B. Brood sorting by ants: distributing the workload over the work surface. Behav. Ecol. Sociobiol. 30, 109–123 ( 1992).

    Article  Google Scholar 

  17. 17

    Deneubourg, J. -L. et al. in Proc. 1st Conf. Simulation of Adaptive Behavior: From Animals to Animats (eds Meyer, J. A. & Wilson, S. W.) 356– 365 (MIT Press, Cambridge, MA, 1991).

    Google Scholar 

  18. 18

    Cao, Y. U., Fukunaga, A. S. & Kahng, A. B. Cooperative mobile robotics: antecedents and directions. Autonomous Robots 4, 7– 27 (1997).

    Article  Google Scholar 

  19. 19

    Martinoli, A., Yamamoto, M. & Mondada, F. in: Proc. 4th European Conf. Artificial Life (eds Husbands, P. & Harvey, I.) (MIT Press, Cambridge, MA, 1997).

    Google Scholar 

  20. 20

    Nilsson, M. & Simsarian, K. T. in Proc. 1995 IEEE/RSJ Intl Conf. Intelligent Robots and Systems 556–561 (IEEE Computer Society Press, Los Alamitos, 1995).

    Google Scholar 

  21. 21

    Kube, C. R. & Zhang, H. Collective robotics: from social insects to robots. Adaptive Behavior 2, 189– 218 (1994).

    Article  Google Scholar 

  22. 22

    Kube, C. R. & Zhang, H. Task modelling in collective robotics. Autonomous Robots 4, 53– 72 (1997).

    Article  Google Scholar 

  23. 23

    Sudd, J. H. The transport of prey by ants. Behaviour 25, 234–271 (1965).

    CAS  Article  Google Scholar 

  24. 24

    Franks, N. R. Teams in social insects: group retrieval of prey by army ants (Eciton burchelli , Hymenoptera: Formicidae). Behav. Ecol. Sociobiol. 18, 425–429 (1986).

    Article  Google Scholar 

  25. 25

    Moffett, M. W. Cooperative food transport by an asiatic ant. Nat. Geogr. Res. 4, 386–394 ( 1988).

    Google Scholar 

  26. 26

    Kelly, K. New Rules for the New Economy (Viking, New York, 1998 ).

    Google Scholar 

  27. 27

    Walters, T. in Proc. PPSN V, Conference on Parallel Problem-Solving from Nature (eds Eiben, A. E., Bäck, T., Schoenauer, M. & Schwefel, H.-S.) 813 –822 (Springer, Berlin, 1998).

    Google Scholar 

  28. 28

    Stützle, T. & Hoos, H. in Proc. IEEE Intl Conf. Evolutionary Computation (eds Bäck, T., Michalewicz,Z. & Yao,X.), 309–314 (IEEE Computer Society Press, Los Alamitos, 1997).

    Google Scholar 

  29. 29

    Van der Put, R. & Rothkrantz, L. in Simulation Practice and Theory (in the press).

  30. 30

    Costa, D. & Hertz, A. Ants can colour graphs. J. Op. Res. Soc. 48, 295–305 (1997).

    Article  Google Scholar 

  31. 31

    Michel, R. & Middendorf, M. in Proc. PPSN V, Conference on Parallel Problem-Solving from Nature (eds Eiben, A. E., Bäck, T., Schoenauer, M. & Schwefel, H.-S.) 692–701 (Springer, Berlin, 1998).

    Google Scholar 

  32. 32

    Gambardella, L. M., Taillard, E. D. & Dorigo, M. Ant colonies for the quadratic assignment problem. J. Op. Res. Soc. 50, 167–176 (1999).

    Article  Google Scholar 

  33. 33

    Stützle, T. & Hoos, H. in Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization (eds Voss, S., Martello, S., Osman, I. H. & Roucairol, C.) 313– 329 (Kluwer Academic, Boston, 1999).

    Book  Google Scholar 

  34. 34

    Maniezzo, V. & Colorni, A. The ant system applied to the quadratic assignment problem. IEEE Trans. Knowledge Data Engin. 11, 769–778 (1999).

    Article  Google Scholar 

  35. 35

    Bauer, A., Bullnheimer, B., Hartl, R. F. & Strauss, C. in Proc. Congr. Evolutionary Computation (CEC’99) 1445– 1450 (IEEE Press, Piscataway, NJ, 1999).

    Google Scholar 

  36. 36

    Bullnheimer, B., Hartl, R. F. & Strauss, C. in Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization (eds Voss, S., Martello, S., Osman, I. H. & Roucairol, C.) 109–120 (Kluwer Academic, Boston, 1999).

    Google Scholar 

  37. 37

    Gambardella, L. M., Taillard, E. D. & Agazzi, G. in New Ideas in Optimization (eds Corne, D., Dorigo, M. & Glover, F.) 63–76 (McGraw-Hill, London, 1999).

    Google Scholar 

  38. 38

    Leguizamón, G. & Michalewicz, Z. in Proc. Congr. Evolutionary Computation (CEC’99) 1459– 1464 (IEEE Press, Piscataway, NJ, 1999).

    Google Scholar 

  39. 39

    Maniezzo, V. & Carbonaro, A. An ANTS heuristic for the frequency assignment problem. Future Generation Comput. Syst. J. 16, 927–935 (2000).

    Article  Google Scholar 

  40. 40

    Gambardella, L. M. & Dorigo, M. Ant colony system hybridized with a new local search for the sequential ordering problem. INFORMS J. Comput. (in the press).

  41. 41

    Bertsekas, D. & Gallager, R. Data Networks (Prentice Hall, Englewood Cliffs, 1992).

    MATH  Google Scholar 

Download references

Acknowledgements

E. B. is supported by the Interval Research Fellowship at the Santa Fe Institute. E. B. and G.T. are supported in part by a grant from the GIS (Groupement d'Intérêt Scientifique) Sciences de la Cognition. G. T. is supported by a grant from the Conseil Régional Midi-Pyrénées. M. D. acknowledges support from the Belgian FNRS, of which he is a Research Associate.

Author information

Affiliations

Authors

Corresponding author

Correspondence to E. Bonabeau.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Bonabeau, E., Dorigo, M. & Theraulaz, G. Inspiration for optimization from social insect behaviour. Nature 406, 39–42 (2000). https://doi.org/10.1038/35017500

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing