An ACO-Based Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems

  • Madjid Khichane
  • Patrick Albert
  • Christine Solnon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5851)


We introduce two reactive frameworks for dynamically adapting some parameters of an Ant Colony Optimization (ACO) algorithm. Both reactive frameworks use ACO to adapt parameters: pheromone trails are associated with parameter values; these pheromone trails represent the learnt desirability of using parameter values and are used to dynamically set parameters in a probabilistic way. The two frameworks differ in the granularity of parameter learning. We experimentally evaluate these two frameworks on an ACO algorithm for solving constraint satisfaction problems.


Local Search Search Process Constraint Satisfaction Problem Pheromone Trail Complete Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dorigo, M., Stuetzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  2. 2.
    Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization. Operations research/Computer Science Interfaces. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  3. 3.
    Tsang, E.: Foundations of Constraint Satisfaction. Academic Press, London (1993)Google Scholar
  4. 4.
    Freuder, E., Wallace, R.: Partial constraint satisfaction. Artificial Intelligence 58, 21–70 (1992)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Dorigo, M., Gambardella, L.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)CrossRefGoogle Scholar
  6. 6.
    Stützle, T., Hoos, H.: \(\cal MAX-MIN\) Ant System. Journal of Future Generation Computer Systems 16, 889–914 (2000)CrossRefGoogle Scholar
  7. 7.
    Solnon, C.: Combining two pheromone structures for solving the car sequencing problem with Ant Colony Optimization. European Journal of Operational Research (EJOR) 191, 1043–1055 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Solnon, C., Fenet, S.: A study of aco capabilities for solving the maximum clique problem. Journal of Heuristics 12(3), 155–180 (2006)zbMATHCrossRefGoogle Scholar
  9. 9.
    Solnon, C.: Ants can solve constraint satisfaction problems. IEEE Transactions on Evolutionary Computation 6(4), 347–357 (2002)CrossRefGoogle Scholar
  10. 10.
    Solnon, C., Bridge, D.: An ant colony optimization meta-heuristic for subset selection problems. In: System Engineering using Particle Swarm Optimization, Nova Science, pp. 7–29 (2006)Google Scholar
  11. 11.
    Minton, S., Johnston, M., Philips, A., Laird, P.: Minimizing conflicts: a heuristic repair method for constraint satistaction and scheduling problems. Artificial Intelligence 58, 161–205 (1992)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    van Dongen, M., Lecoutre, C., Roussel, O.: Results of the second csp solver competition. In: Second International CSP Solver Competition, pp. 1–10 (2007)Google Scholar
  13. 13.
    Battiti, R., Protasi, M.: Reactive local search for the maximum clique problem. Algorithmica 29(4), 610–637 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Randall, M.: Near parameter free ant colony optimisation. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 374–381. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Madjid Khichane
    • 1
    • 2
  • Patrick Albert
    • 1
  • Christine Solnon
    • 2
  1. 1.IBM FranceGentillyFrance
  2. 2.LIRIS CNRS UMR5205Université Lyon 1France

Personalised recommendations