Abstract
Consultant-Guided Search (CGS) is a recent metaheuristic for combinatorial optimization problems, which has been successfully applied to the Traveling Salesman Problem (TSP). In experiments without local search, it has been able to outperform some of the best Ant Colony Optimization (ACO) algorithms. However, local search is an important part of any ACO algorithm and a comparison without local search can be misleading. In this paper, we investigate if CGS is still able to compete with ACO when all algorithms are combined with local search. In addition, we propose a new variant of CGS for the TSP, which introduces the concept of confidence in relation to the recommendations made by consultants. Our experimental results show that the solution quality obtained by this new CGS algorithm is comparable with or better than that obtained by Ant Colony System and MAX-MIN Ant System with 3-opt local search.
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References
Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: Proceedings of GECCO 2002, pp. 11–18 (2002)
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)
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: An Automatic Algorithm Configuration Framework. Journal of Artificial Intelligence Research (JAIR) 36, 267–306 (2009)
Iordache, S.: Consultant-Guided Search - A New Metaheuristic for Combinatorial Optimization Problems. In: Proceedings of the 2010 Genetic and Evolutionary Computation Conference (GECCO 2010). ACM Press, New York (2010)
Ridge, E., Kudenko, D.: Determining whether a problem characteristic affects heuristic performance. A rigorous Design of Experiments approach. In: Recent Advances in Evolutionary Computation for Combinatorial Optimization, vol. 153, pp. 21–35. Springer, Heidelberg (2008)
Stützle, T., Hoos, H.H.: MAX-MIN Ant System. Future Generation Computer Systems 16(8), 889–914 (2000)
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Iordache, S. (2010). Consultant-Guided Search Algorithms with Local Search for the Traveling Salesman Problem. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_9
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DOI: https://doi.org/10.1007/978-3-642-15871-1_9
Publisher Name: Springer, Berlin, Heidelberg
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