Abstract
Stochastic local search algorithms require finding an appropriate setting of their parameters in order to reach high performance. The parameter tuning approaches that have been proposed in the literature for this task can be classified into two families: on-line and off-line tuning. In this paper, we compare the results we achieved with these two approaches. In particular, we report the results of an experimental study based on a prominent ant colony optimization algorithm, \(\mathcal{MAX}\) – \(\mathcal{MIN}\) Ant System, for the traveling salesman problem. We observe the performance of on-line parameter tuning for different parameter adaptation schemes and for different numbers of parameters to be tuned. Our results indicate that, under the experimental conditions chosen here, off-line tuned parameter settings are preferable.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adenso-Díaz, B., Laguna, M.: Fine-tuning of algorithms using fractional experimental designs and local search. Operations Research 54(1), 99–114 (2006)
Anghinolfi, D., Boccalatte, A., Paolucci, M., Vecchiola, C.: Performance evaluation of an adaptive ant colony optimization applied to single machine scheduling. In: Li, X., Kirley, M., Zhang, M., Green, D., Ciesielski, V., Abbass, H.A., Michalewicz, Z., Hendtlass, T., Deb, K., Tan, K.C., Branke, J., Shi, Y. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 411–420. Springer, Heidelberg (2008)
Balaprakash, P., Birattari, M., Stützle, T.: Improvement strategies for the F-race algorithm: Sampling design and iterative refinement. In: Bartz-Beielstein, T., et al. (eds.) HM 2007. LNCS, vol. 4771, pp. 108–122. Springer, Heidelberg (2007)
Battiti, R., Brunato, M., Mascia, F.: Reactive Search and Intelligent Optimization. Operations Research/Computer Science Interfaces, vol. 45. Springer, Berlin (2008)
Birattari, M.: Race. R package (2003), http://cran.r-project.org
Birattari, M.: On the estimation of the expected performance of a metaheuristic on a class of instances. How many instances, how many runs? Tech. Rep. TR/IRIDIA/2004-01, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium (2004)
Birattari, M.: Tuning Metaheuristics: A Machine Learning Perspective. Springer, Berlin (2009)
Birattari, M., Dorigo, M.: How to assess and report the performance of a stochastic algorithm on a benchmark problem: Mean or best result on a number of runs? Optimization Letters 1(3), 309–311 (2007)
Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Langdon, W., et al. (eds.) GECCO 2002, pp. 11–18. Morgan Kaufmann Publishers, San Francisco (2002)
Coy, S., Golden, B., Runger, G., Wasil, E.: Using experimental design to find effective parameter settings for heuristics. Journal of Heuristics 7(1), 77–97 (2001)
Dorigo, M., Gambardella, L.M.: Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter control in evolutionary algorithms. In: [19], pp. 19–46
Förster, M., Bickel, B., Hardung, B., Kókai, G.: Self-adaptive ant colony optimisation applied to function allocation in vehicle networks. In: GECCO 2007, pp. 1991–1998. ACM Press, New York (2007)
Hoos, H.H., Stützle, T.: Stochastic Local Search—Foundations and Applications. Morgan Kaufmann Publishers, San Francisco (2005)
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: An automatic algorithm configuration framework. Journal of Artificial Intelligence Research 36, 267–306 (2009)
Johnson, D., McGeoch, L., Rego, C., Glover, F.: 8th DIMACS implementation challenge (2001), http://www.research.att.com/~dsj/chtsp/
Khichane, M., Albert, P., Solnon, C.: A reactive framework for ant colony optimization. In: Stützle, T. (ed.) LION 3. LNCS, vol. 5851, pp. 119–133. Springer, Heidelberg (2009)
Lobo, F., Lima, C.F., Michalewicz, Z.: Parameter Setting in Evolutionary Algorithms. Springer, Berlin (2007)
Martens, D., Backer, M.D., Haesen, R., Vanthienen, J., Snoeck, M., Baesens, B.: Classification with ant colony optimization. IEEE Transactions on Evolutionary Computation 11(5), 651–665 (2007)
Pellegrini, P., Stützle, T., Birattari, M.: Companion of off-line and on-line tuning: a study on \(\mathcal{MAX-- MIN}\) for TSP (2010) IRIDIA Supplementary page, http://iridia.ulb.ac.be/supp/IridiaSupp2010-008/
Randall, M.: Near Parameter Free Ant Colony Optimisation. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 374–381. Springer, Heidelberg (2004)
Stützle, T.: ACOTSP: A software package of various ant colony optimization algorithms applied to the symmetric traveling salesman problem (2002), http://www.aco-metaheuristic.org/aco-code
Stützle, T., Hoos, H.H.: MAX–MIN ant system. Future Generation Computer Systems 16(8), 889–914 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pellegrini, P., Stützle, T., Birattari, M. (2010). Off-line vs. On-line Tuning: A Study on \(\mathcal{MAX--MIN}\) Ant System for the TSP. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2010. Lecture Notes in Computer Science, vol 6234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15461-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-15461-4_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15460-7
Online ISBN: 978-3-642-15461-4
eBook Packages: Computer ScienceComputer Science (R0)