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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)

Abstract

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.

Keywords

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.

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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

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