An ACO-based reactive framework for ant colony optimization: First experiments on constraint satisfaction problems

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. © 2009 Springer-Verlag.