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The ACO/F-Race Algorithm for Combinatorial Optimization Under Uncertainty

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Metaheuristics

The paper introduces ACO/F-Race, an algorithm for tackling combinatorial optimization problems under uncertainty. The algorithm is based on ant colony optimization and on F-Race. The latter is a general method for the comparison of a number of candidates and for the selection of the best one according to a given criterion. Some experimental results on the PROBABILISTIC TRAVELING SALESMAN PROBLEM are presented.

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Reference

  1. D. J. Bertsimas, P. Jaillet, and A. Odoni. A priori optimization. Operations Research, 38:1019–1033, 1990.

    Article  Google Scholar 

  2. L. Bianchi. Ant Colony Optimization and Local Search for the Probabilistic Traveling Salesman Problem: A Case Study in Stochastic Combinatorial Optimization. PhD thesis, Universit’e Libre de Bruxelles, Brussels, Belgium, 2006.

    Google Scholar 

  3. L. Bianchi, L. M. Gambardella, and M. Dorigo. Solving the homogeneous probabilistic travelling salesman problem by the ACO metaheuristic. In M. Dorigo, G. Di Caro, and M. Sampels, editors, Ant Algorithms, 3rd International Workshop, ANTS 2002, volume 2463 of LNCS, pages 176–187, Berlin, Germany, 2002. Springer-Verlag.

    Google Scholar 

  4. M. Birattari. Race. R package, 2003. http://cran.r-project.org.

    Google Scholar 

  5. M. Birattari. The Problem of Tuning Metaheuristics as Seen from a Machine Learning Perspective. PhD thesis, Universit’e Libre de Bruxelles, Brussels, Belgium, 2004.

    Google Scholar 

  6. M. Birattari, T. Stützle, L. Paquete, and K. Varrentrapp. A racing algorithm for configuring metaheuristics. In W. B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska, editors, Proceedings of the Genetic and Evolutionary Computation Conference, pages 11–18, San Francisco, CA, USA, 2002. Morgan Kaufmann.

    Google Scholar 

  7. W. J. Conover. Practical Nonparametric Statistics. John Wiley & Sons, New York, NY, USA, third edition, 1999.

    Google Scholar 

  8. M. Dorigo and T. Stützle. Ant Colony Optimization. MIT Press, Cambridge, MA, USA, 2004.

    Google Scholar 

  9. W. J. Gutjahr. A converging ACO algorithm for stochastic combinatorial optimization In A. Albrecht, and T. Steinhöfl, editors, Proc. SAGA 2003, volume 2827 of LNCS, pages 10–25, Berlin, Germany, 2003. Springer-Verlag.

    Google Scholar 

  10. W. J. Gutjahr. S-ACO: An ant based approach to combinatorial optimization under uncertainity. In M. Dorigo, M. Birattari, C. Blum, L. M. Gambardella, F. Mondada, and T. Stützle, editors, Ant Colony Optimization and Swarm Intelligence, 4th International Workshop, ANTS 2004, volume 3172 of LNCS, pages 238–249, Berlin, Germany, 2004. Springer-Verlag.

    Google Scholar 

  11. P. Jaillet. Probabilistic Travelling Salesman Problems. PhD thesis, The Massachusetts Institute of Technology, Cambridge, MA, USA, 1995.

    Google Scholar 

  12. D. S. Johnson, L. A. McGeoch, C. Rego, and F. Glover. 8th DIMACS implementation challenge. http://www.research.att.com/ dsj/chtsp/, 2001.

    Google Scholar 

  13. O. Maron and A. W. Moore. Hoeffding races: Accelerating model selection search for classification and function approximation. In J. D. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems, volume 6, pages 59–66, San Francisco, CA, USA, 1994. Morgan Kaufmann.

    Google Scholar 

  14. A. W. Moore and M. S. Lee. Efficient algorithms for minimizing cross validation error. In Proceedings of the Eleventh International Conference on Machine Learning, pages 190–198, San Francisco, CA, USA, 1994. Morgan Kaufmann.

    Google Scholar 

  15. T. Stützle. ACOTSP, version 1.0. http://www.aco-metaheuristic.org/aco-code/, 2002.

    Google Scholar 

  16. T. Stützle and H. H. Hoos. MAX–MIN ant system. Future Generation Computer Systems, 16(8):889–914, 2000.

    Article  Google Scholar 

  17. S. Holm. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, volume 6, 65–70,1979.

    Google Scholar 

  18. F. Rossi and I. Gavioli. Aspects of heuristic methods in the probabilistic traveling salesman problem. Advanced School on Stochastics in Combinatorial Optimization. pages 214–227. World Scientific, Hackensack, NJ, USA, 1987.

    Google Scholar 

  19. S. M. Weiss and C. Kulikowski Computer systems that learn. Classification and prediction methods from statistics neural nets machine learning and expert systems. Morgan Kaufmann, San Mateo, CA, USA, 1991.

    Google Scholar 

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Birattari, M., Balaprakash, P., Dorigo, M. (2007). The ACO/F-Race Algorithm for Combinatorial Optimization Under Uncertainty. In: Doerner, K.F., Gendreau, M., Greistorfer, P., Gutjahr, W., Hartl, R.F., Reimann, M. (eds) Metaheuristics. Operations Research/Computer Science Interfaces Series, vol 39. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-71921-4_10

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