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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
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : 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.
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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⟩

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