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Swarm Intelligence: The Ant Paradigm

  • Chrysostomos Fountas
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 2)

Abstract

This chapter presents the concepts of Swarm Intelligence and the algorithmic framework of ACO for ant algorithms. The basic ideas governing swarms are analyzed as well as the principles of Self-Organization and Stigmergy in the context of ant algorithms. The ACO framework is concisely portrayed and the three most researched ant algorithms, the Ant System, MAX-MIN Ant System, Ant Colony System algorithms, are critically examined in relation to the significant design changes among them.

Keywords

Ant Colony Optimization Ant Algorithms Swarm Intelligence Ant System MAX-MIN Ant System Ant Colony System 

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References

  1. 1.
    Beni, G., Wang, J.: Swarm Intelligence in Cellular Robotic Systems. In: Proceed. NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June 26–30 (1989)Google Scholar
  2. 2.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for optimization from social insect behaviour. Nature 406, 39–42 (2000)CrossRefGoogle Scholar
  3. 3.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence, From Natural to Artificial Systems. Oxford University Press, Oxford (1999)zbMATHGoogle Scholar
  4. 4.
    Camazine, S., Denenbourg, J.L., Franks, N.R., Shyed, J., Theraulaz, G., Bounabeau, E. (eds.): Self-Organization in Biological Systems. Princeton University Press. Princeton (2001)Google Scholar
  5. 5.
    Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Varela, F.J., Bourgine, P. (eds.) Proceedings of the first european conference on artificial life, pp. 134–142. MIT Press, Cambridge (1992a)Google Scholar
  6. 6.
    Deneubourg, J.-L., Aron, S., Goss, S., Pasteels, J.M.: The Self-organizing exploratory pattern of the Argentine ant. Journal of Insect Behaviour 3, 159–168 (1990)CrossRefGoogle Scholar
  7. 7.
    Dorigo, M.: Optimization, Learning and Natural Algorithms. PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy (1992)Google Scholar
  8. 8.
    Dorigo, M., Bonabeau, E., Theraulaz, G.: Ant algorithms and stigmergy. Future Generation Computer Systems 16, 851–871 (2000)CrossRefGoogle Scholar
  9. 9.
    Dorigo, M., Maniezzo, V., Colorni, A.: Positive feedback as a search strategy. Technical Report 91-016, Dipartimento di Elettronica, Politecnico di Milano, Italy (1991a)Google Scholar
  10. 10.
    Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: An Autocatalytic Optimizing Process. Technical Report 91-016, Dipartimento di Elettronica, Politecnico di Milano, Italy (1991b)Google Scholar
  11. 11.
    Dorigo, M., Di Caro, G.: Ant Colony Optimization: A new meta-heuristic. In: Angeline, P.J., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A. (eds.) Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 1470–1477. IEEE Press, May_ower Hotel (1999)Google Scholar
  12. 12.
    Dorigo, M., Gambardella, L.M.: Ant Colony System: A cooperative learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  13. 13.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  14. 14.
    Fainekos, G.: Ant Colony Optimization: Applications in Discrete and Continuous Problems, Diploma Thesis. Department of Mechanical Engineering, National Technical University of Athens (2001)Google Scholar
  15. 15.
    Gambardella, L.M., Dorigo, M.: Solving symmetric and asymmetric TSPs by ant colonies. In: Beack, T., Fukuda, T., Michalewicz, Z. (eds.) Procceedings of the 1996 IEEE International Conference on Evolutionary Computation (ICEC 96), pp. 622–627. IEEE Press, Piscataway (1996)CrossRefGoogle Scholar
  16. 16.
    Goss, S., Aron, S., Deneubourg, J.L., Pasteels, J.M.: Self-organized shortcuts in the Argentine ant. Naturwissenschaften 76, 579–581 (1989)CrossRefGoogle Scholar
  17. 17.
    Grassé, P.P.: La Reconstruction du nid et les coordinations interindividuelles chez, Bellicositermes Natalensis et Cubitermes sp. La théorie de la stigmergie: Essai d’ interprétation du comportement des termites constructeurs. Insectes-Sociaux 6, 41–80 (1959)Google Scholar
  18. 18.
    Nicolis, G., Prigogine, I.: Self-organization in Non-Equilibrium Systems. Willey & Sons, Chichester (1977)Google Scholar
  19. 19.
    Stutzle, T., Hoos, H.H.: MAX-MIN Ant System. Future Generation of Computer Systems 16(8), 889–914 (2000)CrossRefGoogle Scholar
  20. 20.
    Stutzle, T., Hoos, H.: The MAX-MIN Ant System and Local Search for the Traveling Salesman Problem. In: Back, T., Michalewizc, Z., Yao, X. (eds.) Proceedings of the 1997 IEEE International Conference on Evolutionary Computation (ICEC 1997), pp. 309–314. IEEE Press, Los Alamitos (1997)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2010

Authors and Affiliations

  • Chrysostomos Fountas
    • 1
  1. 1.Department of InformaticsUniversity of PiraeusPiraeusGreece

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