Skip to main content
SpringerLink
Log in

Application of ant colony optimization to adaptive routing in aleo telecomunications satellite network

Utilisation d’agents mobiles de type « fourmis » pour le routage dans une constellation de satellites de télécommunication

  • Published:
Annales Des Télécommunications Aims and scope Submit manuscript

Abstract

Ant colony optimization (Aco) has been proposed as a promising tool for adaptive routing in telecommunications networks. The algorithm is applied here to a simulation of a satellite telecommunications network with 72Leo nodes and 121 earth stations. Three variants ofAco are tested in order to assess the relative importance of the different components of the algorithm. The bestAco variant consistently gives performance superior to that obtained with a standard link state algorithm (Spf), under a variety of traffic conditions, and at negligible cost in terms of routing bandwidth.

Résumé

Une méthode d’optimisation utilisant des agents de type“fourmis”(ant colony optimi zation) est proposée pour les problèmes de routage dynamique dans les réseaux de télécommunications. L’algorithme est appliqué à un réseau de satellites comprenant 72 satellites leo et 121 stations terriennes. Trois versions de Valgorithme sont comparées dans le but d’évaluer l’importance relative des différentes composantes de l’algorithme. La version complète de l’algorithme donne de façon systématique des résultats meilleurs que ceux obtenus par Valgorithme standard spf, ceci pour différentes conditions de trafic, et un coût moindre en termes de bande passante.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. 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.

    Article  Google Scholar 

  2. Schoonderwoerd (R.), Holland (O.), Brüten (J.), « Ant-like agents for load balancing in telecommunications networks »,Proceedings of Agents ’97, Marina del Rey, CA, USA, ACM, Inc., Eds., p.209- 216, 1997.

    Chapter  Google Scholar 

  3. Di Caro (G.), Dorigo (M.), « AntNet: A mobile agents approach to adaptive routing »,Technical Report IR1DIA97-12, Université Libre de Bruxelles, 1997.

  4. Di Caro (G.), Dorigo (M.), « Mobile agents for adaptive routing »,Proceedings of 31s’ Hawaii International Conference on Systems Sciences (HICSS-3I), Hawaii, January, 1998.

  5. Di Caro (G.), Dorigo (M.), « AntNet: distributed stigmeric control for communications networks »,Journal of Artificial Intelligence Research,9:317–365, 1998.

    MATH  Google Scholar 

  6. Bonabeau (E.), Heneaux (F.), Guérin (S.), Snyers (D.), Kuntz (P.), Theraulaz (G.), « Routing in telecommunications networks with smart ant-like agents »,Santa Fe Institute Working Paper 98-01- 003, 1998.

  7. Heusse (M.), Snyers (D.), Guérin (S.), Kuntz (P.), « Adaptive agent-driven routing and load balan- cing in communication networks »,Ecole Nationale Supérieure des Télécommunications de Bretagne Technical Document RR-9800I-IASC, 1998.

  8. Maniezzo (V), Colorni (A.), Dorigo (M.), « The ant system applied to the quadratic assignment pro- blem »,Technical Report 1R1DIAI94-28, Université Libre de Bruxelles, 1994.

  9. Colorni (A.), Dorigo (M.), Maffioli (F.), Maniezzo (V), Righini (G.), Trubian (M.), « Heuristics from Nature for Hard Combinatorial Problems »,International Transactions in Operational Research,3(1): 1–21, 1996.

    Article  MATH  Google Scholar 

  10. Costa (D.), Hertz (A.), « Ants Can Colour Graphs »,Journal of the Operational Research Society,48:295–305, 1997.

    Article  MATH  Google Scholar 

  11. Dorigo (M.), Gambardella (L.M.), « Ant colony sytem: A cooperative learning approach to the travelling salesman problem »,IEEE Transactions on Evolutionary Computation,1(1): 53–66, 1997.

    Article  Google Scholar 

  12. Dorigo (M.), Di Caro (G.), Gambardella (L.M.), « Ant Algorithms for Discrete Optimization »,Artificial Life,5(2): 137–172, 1999.

    Article  Google Scholar 

  13. Gambardella (L.M.), Taillard (E.), Dorigo (M.), « Ant Colonies for the Quadratic Assignment Problem »,Journal of the Operational Research Society,50:167–176, 1999.

    Article  MATH  Google Scholar 

  14. Project Overview, 1999, “Constellation de Satellites pour le Multimedia” of the Réseau National de Recherche en Télécommunications (Rnrt) of France; see also http://constellation.prism.uvsq.fr.

  15. Werner M. and Maral G., « Traffic flows and dynamic routing in leo intersatellite link networks »,Proceedings of the International Mobile Satellite Conference (IMSC ’97), p. 283–288, Pasadena, California, USA, June, 1997.

Download references

Author information

Authors and Affiliations

  1. CETP/CNRS, 10 avenue de l’Europe, F-78140, Vélizy, France

    Eric Sigel, Bruce Denby & Sylvie Le Hégarat-Mascle

Authors
  1. Eric Sigel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bruce Denby
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sylvie Le Hégarat-Mascle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Eric Sigel, Bruce Denby or Sylvie Le Hégarat-Mascle.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sigel, E., Denby, B. & Le Hégarat-Mascle, S. Application of ant colony optimization to adaptive routing in aleo telecomunications satellite network. Ann. Télécommun. 57, 520–539 (2002). https://doi.org/10.1007/BF02995174

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02995174

Key words

Mots clés

Search

Navigation