A New Approach to Improve the Ant Colony System Performance: Learning Levels

Authors:
Laura Cruz R.;Juan J. Gonzalez B.;José F. Orta;Barbara A. Arrañaga C.;Hector J. Fraire H.
Affiliations:
Instituto Tecnológico de Ciudad Madero, Ciudad Madero Tamaulipas, México CP. 89100.;Instituto Tecnológico de Ciudad Madero, Ciudad Madero Tamaulipas, México CP. 89100.;Instituto Tecnológico de Ciudad Madero, Ciudad Madero Tamaulipas, México CP. 89100.;Instituto Tecnológico de Ciudad Madero, Ciudad Madero Tamaulipas, México CP. 89100.;Instituto Tecnológico de Ciudad Madero, Ciudad Madero Tamaulipas, México CP. 89100.
Venue:
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Year:
2009

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Abstract

In this paper a hybrid ant colony system algorithm is presented. A new approach to update the pheromone trails, denominated learning levels, is incorporated. Learning levels is based on the distributed Q-learning algorithm, a variant of reinforcement learning, which is incorporated to the basic ant colony algorithm. The hybrid algorithm is used to solve the Vehicle Routing Problem with Time Windows. Experimental results with the Solomon's dataset of instances reveal that learning levels improve execution time and quality, respect to the basic ant colony system algorithm, 0.15% for traveled distance and 0.6% in vehicles used. Now we are applying the hybrid ant colony system in other domains.