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

Madjid Khichane (1) , Patrick Albert (2) , Christine Solnon (1)

1 M2DisCo - Geometry Processing and Constrained Optimization
2 ILOG SA [Gentilly]

We introduce two reactive frameworks for dynamic adapating 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.

Domaines: Informatique [cs]

Madjid Khichane, Patrick Albert, Christine Solnon. An ACO-Based Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems. Learning and Intelligent OptimizatioN (LION), Jan 2009, Trento, Italy. pp.119-133, ⟨10.1007/978-3-642-11169-3_9⟩. ⟨hal-01437618⟩