Abstract
Applying parameter adaptation means operating on parameters of an algorithm while it is tackling an instance. For ant colony optimization, several parameter adaptation methods have been proposed. In the literature, these methods have been shown to improve the quality of the results achieved in some particular contexts. In particular, they proved to be successful when applied to novel ant colony optimization algorithms for tackling problems that are not a classical testbed for optimization algorithms. In this paper, we show that the adaptation methods proposed so far do not improve, and often even worsen the performance when applied to high performing ant colony optimization algorithms for some classical combinatorial optimization problems.
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References
Angeline, P. J. (1995). Adaptive and self-adaptive evolutionary computations. In M. Palaniswami et al. (Eds.), Computational intelligence: a dynamic systems perspective (pp. 152–163). New York: IEEE Press.
Anghinolfi, D., Boccalatte, A., Paolucci, M., & Vecchiola, C. (2008). Performance evaluation of an adaptive ant colony optimization applied to single machine scheduling. In X. Li et al. (Eds.), LNCS: Vol. 5361. SEAL (pp. 411–420). Heidelberg: Springer.
Applegate, D., Bixby, R., Chvátal, V., & Cook, W. (2003). Concorde package. www.tsp.gatech.edu/concorde/downloads/codes/src/co031219.tgz.
Birattari, M. (2004). 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.
Birattari, M. (2009). Tuning metaheuristics: a machine learning perspective. Studies of computational intelligence (Vol. 197). Berlin: Springer.
Birattari, M., Zlochin, M., & Dorigo, M. (2006). Towards a theory of practice in metaheuristics design: A machine learning perspective. Theoretical Informatics and Applications, 40(2), 353–369.
Birattari, M., Stützle, T., Paquete, L., & Varrentrapp, K. (2002). A racing algorithm for configuring metaheuristics. In W. Langdon et al. (Eds.), GECCO 2002 (pp. 11–18). San Francisco: Morgan Kaufmann.
Clerc, M. (2006). Particle swarm optimization. London: ISTE.
Dorigo, M., & Gambardella, L. M. (1997). Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53–66.
Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge: MIT Press.
Eiben, A. E., Michalewicz, Z., Schoenauer, M., & Smith, J. E. (2007). Parameter control in evolutionary algorithms. In F. G. Lobo et al. (Eds.), Studies in computational intelligence: Vol. 54. Parameter setting in evolutionary algorithms (pp. 19–46). Berlin: Springer.
Fialho, A. (2010). Adaptive operator selection for optimization. PhD thesis, Université Paris-Sud XI, Orsay, France.
Förster, M., Bickel, B., Hardung, B., & Kókai, G. (2007). Self-adaptive ant colony optimisation applied to function allocation in vehicle networks. In GECCO’07 (pp. 1991–1998). New York: ACM.
Hussin, M. S., & Stützle, T. (2010). Tabu search vs. simulated annealing for solving large quadratic assignment instances (Tech. Rep. TR/IRIDIA/2010-20). IRIDIA, Université Libre de Bruxelles, Brussels, Belgium.
Johnson, D., McGeoch, L., Rego, C., & Glover, F. (2001). 8th DIMACS implementation challenge. http://www.research.att.com/~dsj/chtsp/.
Khichane, M., Albert, P., & Solnon, C. (2009). A reactive framework for ant colony optimization. In T. Stützle (Ed.), LNCS: Vol. 5851. Learning and intelligent optimizatioN (LION) (pp. 119–133). Heidelberg: Springer.
Lawler, E. L. (1963). The quadratic assignment problem. Management Science, 9, 586–599.
Lawler, E. L., Lenstra, J. K., Rinnooy, Kan A. H. G., & Shmoys, D. B. (1985). The traveling salesman problem: a guided tour of combinatorial optimization. New York: Wiley.
López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., & Birattari, M. (2011). The irace package, iterated race for automatic algorithm configuration (Tech. Rep. TR/IRIDIA/2011-04). IRIDIA, Université Libre de Bruxelles, Brussels, Belgium.
Martens, D., Backer, M. D., Haesen, R., Vanthienen, J., Snoeck, M., & Baesens, B. (2007). Classification with ant colony optimization. IEEE Transactions on Evolutionary Computation, 11(5), 651–665.
Pellegrini, P., Stützle, T., & Birattari, M. (2010a). Companion of a critical analysis of parameter adaptation in ant colony optimization. http://iridia.ulb.ac.be/supp/IridiaSupp2010-013/. IRIDIA Supplementary page, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium.
Pellegrini, P., Stützle, T., & Birattari, M. (2010b). Off-line and on-line tuning: a study on \(\mathcal{M}\mathcal{A}\mathcal{X}\)–\(\mathcal{M}\mathcal{I}\mathcal{N}\) ant system for TSP. In M. Dorigo et al. (Eds.), LNCS: Vol. 6234. ANTS 2010: seventh international conference on swarm intelligence (pp. 239–250). Heidelberg: Springer.
Randall, M. (2004). Near parameter free ant colony optimisation. In M. Dorigo et al. (Eds.), LNCS: Vol. 3172. ANTS 2004: fourth international conference on ant colony optimization and swarm intelligence (pp. 374–381). Heidelberg: Springer.
Stützle, T. (2002). ACOTSP: A software package of various ant colony optimization algorithms applied to the symmetric traveling salesman problem. http://www.aco-metaheuristic.org/aco-code.
Stützle, T., & Hoos, H. H. (2000). MAX–MIN ant system. Future Generations Computer Systems, 16(8), 889–914.
Stützle, T., López-Ibánez, M., Pellegrini, P., Maur, M., Montes de Oca, M. A., Birattari, M., & Dorigo, M. (2011, to appear). Parameter adaptation in ant colony optimization. In Y. Hamadi et al. (Eds.), Autonomous search. Berlin: Springer.
Taillard, E. (1991). Robust taboo search for the quadratic assignment problem. Parallel Computing, 17, 443–455.
Taillard, E. (1995). Comparison of iterative searches for the quadratic assignment problem. Location Science, 3, 87–105.
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83.
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Pellegrini, P., Stützle, T. & Birattari, M. A critical analysis of parameter adaptation in ant colony optimization. Swarm Intell 6, 23–48 (2012). https://doi.org/10.1007/s11721-011-0061-0
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DOI: https://doi.org/10.1007/s11721-011-0061-0