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Published in:
2009 | OriginalPaper | Chapter
An ACO-Based Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems
Authors : Madjid Khichane, Patrick Albert, Christine Solnon
Published in: Learning and Intelligent Optimization
Publisher: Springer Berlin Heidelberg
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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.