Ant colony optimization with group learning
Pages 57 - 64
Abstract
We introduce Group Learning for Ant Colony Optimization applied to combinatorial optimization problems with group-structured solution encodings. In contrast to the common assignment of one pheromone value per solution component in Group Learning each solution component has one pheromone value per group. Hence, the algorithm has the possibility to learn the optimal group membership of the components. We present different strategies for Group Learning and evaluate these in simulation experiments for the Vehicle Routing Problem with Time Windows using the problem instances of Solomon. We describe a revised Ant Colony System (ACS) algorithm which does not use a local pheromone update while maintaining the general ideas of ACS. We evaluate the revised ACS experimentally comparing it to the original ACS. Our experimental results show that Group Learning is a valuable modification for Ant Colony Optimization. Additionally, the results indicate that the revised ACS performs at least as well as the original algorithms.
Supplementary Material
The included supplement.pdf contains the parameter ranges for the tuning via irace (http://iridia.ulb.ac.be/irace/) and the best found parameter values.
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References
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M. Birattari. phThe Problem of Tuning Metaheuristics as Seen from a Machine Learning Perspective. PhD thesis, Université Libre de Bruxelles, Brussels, Belgium, 2004.
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O. Bräysy and M. Gendreau. Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms. phTransportation Science, 39 (1): 104--118, 2005.
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M. Dorigo and L. Gambardella. Ant Colony System: A cooperative learning approach to the traveling salesman problem. phIEEE Transactions on Evolutionary Computation, 1 (1): 53 --66, 1997.
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M. Dorigo and T. Stützle. phAnt Colony Optimization. Bradford Books. MIT Press, 2004.
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L. M. Gambardella, Éric Taillard, and G. Agazzi. MACS-VRPTW: A Multiple Colony System For Vehicle Routing Problems With Time Windows. In D. Corne, M. Dorigo, F. Glover, D. Dasgupta, P. Moscato, R. Poli, and K. V. Price, editors, phNew Ideas in Optimization, pages 63--76. McGraw-Hill, 1999.
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M. López-Ibánez, J. Dubois-Lacoste, T. Stützle, and M. Birattari. The irace package, Iterated Race for Automatic Algorithm Configuration. Technical Report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium, 2011.
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Index Terms
- Ant colony optimization with group learning
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Published: 12 July 2014
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GECCO '14 Paper Acceptance Rate 180 of 544 submissions, 33%;
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