Skip to main content

Artificial Ants

  • Chapter
  • First Online:
Metaheuristics

Abstract

Ants are social insects with physical and behavioral skills that are still fascinating to human beings (Greek mythology mentioned them!). This fascination is often justified by biological studies and observations: the activity of ants is undoubtedly observable, such as in the huge nests (anthills) that they build, their battles, and their various diets (their“agriculture” when growing fungi, for instance).

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

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    This paper is linked to Marco Dorigo’s Ph.D. thesis [7].

References

  1. Abraham, A., Grosan, C., Ramos, V. (eds.): Stigmergic Optimization. Studies in Computational Intelligence, vol. 31. Springer (2006)

    Google Scholar 

  2. Baluja, S., Caruana, R.: Removing the genetics from the standard genetic algorithm. In: A. Prieditis, S. Russell (eds.) Proceedings of the Twelfth International Conference on Machine Learning (ICML), pp. 38–46. Morgan Kaufmann, San Mateo, CA (1995)

    Google Scholar 

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

    Google Scholar 

  4. Bullnheimer, B., Hartl, R., Strauss, C.: A new rank based version of the ant system: A computational study. Central European Journal for Operations Research and Economics 7(1), 25–38 (1999)

    Google Scholar 

  5. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: F. Varela, P. Bourgine (eds.) Proceedings of the First European Conference on Artificial Life (ECAL), pp. 134–142. MIT Press, Cambridge, MA (1991)

    Google Scholar 

  6. Deneubourg, J., Goss, S., Pasteels, J., Fresneau, D., Lachaud, J.: Self-organization mechanisms in ant societies (ii): Learning in foraging and division of labor. In: J. Pasteels, J. Deneubourg (eds.) From Individual to Collective Behavior in Social Insects. Experientia supplementum, vol. 54, pp. 177–196. Birkhäuser (1987)

    Google Scholar 

  7. Dorigo, M.: Optimization, learning and natural algorithms [in Italian]. Ph.D. thesis, Politecnico di Milano, Italy (1992)

    Google Scholar 

  8. Dorigo, M., Gambardella, L.: Ant colony sytem: A cooperative learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997). ftp://iridia.ulb.ac.be/pub/mdorigo/journals/IJ.16-TEC97.A4.ps.gz

    Google Scholar 

  9. Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B 26(1), 29–41 (1996)

    Google Scholar 

  10. Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge, MA (2004)

    Google Scholar 

  11. Goss, S., Aron, S., Deneubourg, J., Pasteels, J.: Self-organized shortcuts in the Argentine ant. Naturwissenchaften 76, 579–581 (1989)

    Google Scholar 

  12. Goss, S., Fresneau, D., Deneubourg, J., Lachaud, J., Valenzuela-Gonzalez, J.: Individual foraging in the ant Pachycondyla apicalis. Œcologia 80, 65–69 (1989)

    Google Scholar 

  13. Gravel, M., Gagné, C.: Ant colony optimization for manufacturing aluminum bars. In: N. Monmarché, F. Guinand, P. Siarry (eds.) Artificial Ants. Wiley-Blackwell (2010)

    Google Scholar 

  14. Gutjahr, W.: A graph-based ant system and its convergence. Future Generation Computer Systems 16(8), 873–888 (2000)

    Google Scholar 

  15. Gutjahr, W.: ACO algorithms with guaranteed convergence to the optimal solution. Information Processing Letters 82(3), 145–153 (2002)

    Google Scholar 

  16. Manderick, B., Moyson, F.: The collective behavior of ants: An example of self-organization in massive parallelism. In: Proceedings of the AAAI Spring Symposium on Parallel Models of Intelligence. American Association of Artificial Intelligence, Stanford, CA (1988)

    Google Scholar 

  17. Monmarché, N., Guinand, F., Siarry, P. (eds.): Artificial Ants: From Collective Intelligence to Real Life Optimization and Beyond. ISTE-Wiley (2010)

    Google Scholar 

  18. Monmarché, N., Ramat, E., Desbarats, L., Venturini, G.: Probabilistic search with genetic algorithms and ant colonies. In: A. Wu (ed.) Proceedings of the Optimization by Building and Using Probabilistic Models workshop, Genetic and Evolutionary Computation Conference, Las Vegas, pp. 209–211 (2000)

    Google Scholar 

  19. Neumann, F., Witt, C.: Bioinspired Computation in Combinatorial Optimization, Algorithms and Their Computational Complexity. Natural Computing Series. Springer (2010)

    Google Scholar 

  20. Passera, L.: Le monde extraordinaire des fourmis. Fayard (2008)

    Google Scholar 

  21. Stützle, T., Hoos, H.: \({\cal MAX}-{\cal MIN}\) ant system and local search for the traveling salesman problem. In: Proceedings of the Fourth International Conference on Evolutionary Computation (ICEC), pp. 308–313. IEEE Press (1997)

    Google Scholar 

  22. Stützle, T., López-Ibáñez, M., Pellegrini, P., Maur, M., Montes de Oca, M., Birattari, M., Dorigo, M.: Parameter adaptation in ant colony optimization. In: Y. Hamadi, E. Monfroy, F. Saubion (eds.) Autonomous Search, pp. 191–215. Springer, Berlin, Heidelberg (2012). doi:10.1007/978-3-642-21434-9_8

    Google Scholar 

  23. Syswerda, G.: Simulated crossover in genetic algorithms. In: L. Whitley (ed.) Second Workshop on Foundations of Genetic Algorithms, pp. 239–255. Morgan Kaufmann, San Mateo, CA (1993)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nicolas Monmarché .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Monmarché, N. (2016). Artificial Ants. In: Siarry, P. (eds) Metaheuristics. Springer, Cham. https://doi.org/10.1007/978-3-319-45403-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45403-0_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45401-6

  • Online ISBN: 978-3-319-45403-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics