Summary
Developing metaheuristics requires in general a lot of work tuning different parameters. This paper presents a two–level algorithm to tackle this problem: an upper–level algorithm is used to determine the most appropriate set of parameters for a lower–level metaheuristic. This approach is applied to an Ant Colony Optimisation (ACO) metaheuristic that was designed to solve the Orienteering Problem (OP). That is a particular routing problem in which a score is earned for visiting a location. The objective is to maximise the sum of the scores, while not exceeding a given time budget. The ACO algorithm uses a set of ants that communicate through the environment by means of a pheromone trail. The transition rule and pheromone updating rules are influenced by a large number of parameters. These parameters are fine–tuned by a Genetic Algorithm (GA), which trains the ACO using test problems from the literature. The resulting ACO algorithm is compared with an exact algorithm by applying it to another set of problems. The scores obtained by the resulting algorithm are very near the optimal scores for the test problems.
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References
Alba, E., Luna, F., Nebro, A.: Advances in parallel heterogeneous genetic algorithms for continuous optimization. International Journal of Applied Mathematics and Computer Science 14(3), 317–333 (2004)
Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the 2nd International Conference on Genetic Algorithms, Hillsdale, New Jersey, pp. 14–21. Lawrence Erlbaum Associates, Mahwah (1987)
Bullnheimer, B.: Ant Colony Optimization in Vehicle Routing. PhD thesis, University of Vienna (1999)
Chao, I.-M., Golden, B., Wasil, E.: A fast and effective heuristic for the orienteering problem. European Journal of Operational Research 88(3), 475–489 (1996)
Chelouah, R., Siarry, P.: A continuous genetic algorithm designed for the global optimization of multimodal functions. Journal of Heuristics 6, 191–213 (2000)
Costa, D., Hertz, A.: Ants can colour graphs. J. Oper. Res. Soc. 48, 295–305 (1997)
Dantzig, G., Fulkerson, R., Johnson, S.: Solution of a large-scale traveling salesman problem. Operations Research 2, 393–410 (1954)
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comp. 1, 53–66 (1997)
Feillet, D., Dejax, P., Gendreau, M.: Traveling salesman problems with profits. Transportation Science 39, 188–205 (2005)
Gambardella, L.M., Taillard, E.D., Dorigo, M.: Ant colonies for the qap. J. Oper. Res. Soc. 50(2), 167–176 (1999)
Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Kluwer Academic Publishers, Dordrecht (1989)
Golden, B., Levy, L., Vohra, R.: The orienteering problem. Naval Research Logistics 34, 307–318 (1987)
Golden, B., Wang, Q., Liu, L.: A multifaceted heuristic for the orienteering problem. Naval Research Logistics 35, 359–366 (1988)
Herrera, F., Lozano, M.: Gradual distributed real-coded genetic algorithms. IEEE Transactions on Evolutionary Computation 4(1), 43–63 (2000)
Herrera, F., Lozano, M., Verdegay, J.: Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis. Artificial Intelligence Review 12(4), 265–319 (1998)
Leifer, A.C., Rosenwein, M.S.: Strong linear programming relaxations for the orienteering problem. Bell System Technical Journal 44, 2245–2269 (1994)
Levine, D.: Parallel genetic algorithm library, http://www-fp.mcs.anl.gov/CCST/research/reports_pre1998/comp_bio/stalk/pgapack.html
Liang, Y.-C., Smith, A.E.: An ant colony approach to the orienteering problem. Technical report, Department of Industrial and Systems Engineering, Auburn University, Auburn, AL (2001)
Lin, S.: Computer solutions of the traveling salesman problem. Bell System Technical Journal 44, 2245–2269 (1965)
Miller, C.E., Tucker, A.W., Zemlin, R.A.: Integer programming formulations and traveling salesman problems. J. ACM 7, 326–329 (1960)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Reeves, C.: Handbook of Metaheuristics, chapter Genetic Algorithms, pp. 55–82. Kluwer Academic Publishers, Dordrecht (2003)
Tsiligirides, T.: Heuristic methods applied to orienteering. J. Oper. Res. Soc. 35(9), 797–809 (1984)
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Souffriau, W., Vansteenwegen, P., Vanden Berghe, G., Van Oudheusden, D. (2008). Automated Parameterisation of a Metaheuristic for the Orienteering Problem. In: Cotta, C., Sevaux, M., Sörensen, K. (eds) Adaptive and Multilevel Metaheuristics. Studies in Computational Intelligence, vol 136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79438-7_13
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DOI: https://doi.org/10.1007/978-3-540-79438-7_13
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