Abstract
Ant colony optimization performs very well on many hard optimization problems, even though no good worst case guarantee can be given. Understanding the reasons for the performance and the influence of its different parameter settings has become an interesting problem. In this paper, we build a parameter prediction model for the Traveling Salesperson problem based on features of evolved instances. The two considered parameters are the importance of the pheromone values and of the heuristic information. Based on the features of the evolved instances, we successfully predict the best parameter setting for a wide range of instances taken from TSPLIB.
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References
Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6(1), 37–66 (1991)
Applegate, D., Cook, W.J., Dash, S., Rohe, A.: Solution of a Min-Max Vehicle Routing Problem. Journal on Computing 14(2), 132–143 (2002)
Dorigo, M., Stützle, T.: Ant Colony Optimization. Bradford Company (2004)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: An update. SIGKDD Explororations Newsletter 11(1), 10–18 (2009)
Hoos, H.: Automated algorithm configuration and parameter tuning. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds.) Autonomous Search, pp. 37–71. Springer, Heidelberg (2012)
Kötzing, T., Neumann, F., Röglin, H., Witt, C.: Theoretical analysis of two ACO approaches for the traveling salesman problem. Swarm Intelligence 6, 1–21 (2012)
Mersmann, O., Bischl, B., Trautmann, H., Wagner, M., Bossek, J., Neumann, F.: A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem. In: Annals of Mathematics and Artificial Intelligence, pp. 1–32 (2013)
Muñoz, M.A., Kirley, M., Halgamuge, S.K.: A meta-learning prediction model of algorithm performance for continuous optimization problems. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part I. LNCS, vol. 7491, pp. 226–235. Springer, Heidelberg (2012)
Nallaperuma, S., Wagner, M., Neumann, F.: Ant colony optimisation and the traveling salesperson problem: Hardness, features and parameter settings (extended abstract). In: 15th Annual Conference Companion on Genetic and Evolutionary Computation Conference Companion (GECCO Companion), pp. 13–14. ACM (2013)
Nallaperuma, S., Wagner, M., Neumann, F., Bischl, B., Mersmann, O., Trautmann, H.: A Feature-based Comparison of Local Search and the Christofides Algorithm for the Travelling Salesperson Problem. In: International Conference on Foundations of Genetic Algorithms, FOGA (2013)
Pellegrini, P., Favaretto, D., Moretti, E.: On \(\cal M\!AX\!\) – \(\cal MI\!N\!\) ant system’s parameters. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 203–214. Springer, Heidelberg (2006)
Pellegrini, P., Stützle, T., Birattari, M.: Off-line vs. on-line tuning: A study on \(\mathcal{MAX--MIN}\) ant system for the TSP. In: Dorigo, M., Birattari, M., Di Caro, G.A., Doursat, R., Engelbrecht, A.P., Floreano, D., Gambardella, L.M., Groß, R., Şahin, E., Sayama, H., Stützle, T. (eds.) ANTS 2010. LNCS, vol. 6234, pp. 239–250. Springer, Heidelberg (2010)
Reinelt, G.: TSPLIB – A Traveling Salesman Problem Library. ORSA Journal on Computing 3(4), 376–384 (1991)
Ridge, E., Kudenko, D.: Determining Whether a Problem Characteristic Affects Heuristic Performance. In: Cotta, C., van Hemert, J. (eds.) Recent Advances in Evol. Comp. SCI, vol. 153, pp. 21–35. Springer, Heidelberg (2008)
Smith-Miles, K., van Hemert, J., Lim, X.Y.: Understanding TSP difficulty by learning from evolved instances. In: Blum, C., Battiti, R. (eds.) LION 4. LNCS, vol. 6073, pp. 266–280. Springer, Heidelberg (2010)
Stützle, T.: Software package: Acotsp.v1.03.tgz (2012)
Stützle, T., Dorigo, M.: A short convergence proof for a class of Ant Colony Optimization algorithms. IEEE Trans. on Evolutionary Computation, 358–365 (2002)
Stützle, T., Hoos, H.H.: MAX-MIN Ant system. Future Generation Computer Systems 16(9), 889–914 (2000)
Stützle, T., López-Ibáñez, M., Pellegrini, P., Maur, M., Montes de Oca, M., Birattari, M., Dorigo, M.: Parameter Adaptation in Ant Colony Optimization. In: Autonomous Search, pp. 191–215. Springer (2012)
Stützle, T., Hoos, H., Merz, P.: An Analysis of the Hardness of TSP Instances for Two High-performance Algorithms. In: 6th Metaheuristics International Conference (MIC), pp. 361–367 (2005)
Wilcoxon, F.: Individual Comparisons by Ranking Methods. Biometrics Bulletin 1(6), 80–83 (1945)
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Nallaperuma, S., Wagner, M., Neumann, F. (2014). Parameter Prediction Based on Features of Evolved Instances for Ant Colony Optimization and the Traveling Salesperson Problem. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_10
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DOI: https://doi.org/10.1007/978-3-319-10762-2_10
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