Abstract
Ant colony optimization algorithms are currently among the best performing algorithms for the quadratic assignment problem. These algorithms contain two main search procedures: solution construction by artificial ants and local search to improve the solutions constructed by the ants. Incremental local search is an approach that consists in re-optimizing partial solutions by a local search algorithm at regular intervals while constructing a complete solution. In this paper, we investigate the impact of adopting incremental local search in ant colony optimization to solve the quadratic assignment problem. Notwithstanding the promising results of incremental local search reported in the literature in a different context, the computational results of our new ACO algorithm are rather negative. We provide an empirical analysis that explains this failure.
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References
Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Stützle, T., Hoos, H.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco (2005)
Russell, R.: Hybrid Heuristics for the Vehicle Routing Problem with Time Windows. Transportation Science 29, 156–166 (1995)
Gendreau, M., Hertz, A., Laporte, G.: New Insertion and Postoptimization Procedures for the Traveling Salesman Problem. Operations Research 40(6) (1992)
Gendreau, M., Hertz, A., Laporte, G.: A Tabu Search Heuristic for the Vehicle Routing Problem. Management Science 40, 1276–1290 (1994)
Caseau, Y., Laburthe, F.: Heuristics for Large Constrained Vehicle Routing Problems. Journal of Heuristics 5(3), 281–303 (1999)
Fleurent, C., Glover, F.: Improved Constructive Multistart Strategies for the Quadratic Assignment Problem Using Adaptive Memory. INFORMS Journal on Computing 11(2), 198–204 (1999)
Stützle, T., Hoos, H.: \(\cal MAX\)–\(\cal MIN\) Ant System. Future Generation Computer Systems 16(8), 889–914 (2000)
Stützle, T., Dorigo, M.: ACO Algorithms for the Quadratic Assignment Problem. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, London, UK, pp. 33–50. McGraw-Hill, New York (1999)
Burkard, R., Karisch, S., Rendl, F.: http://www.seas.upenn.edu/qaplib
Stützle, T., Hoos, H.H.: Improving the Ant System: A Detailed Report on the \(\cal MAX\)–\(\cal MIN\) Ant System. Technical Report AIDA–96–12, FG Intellektik, FB Informatik, TU Darmstadt (1996)
Schiavinotto, T., Stützle, T.: Metrics on Permutations for Search Space Analysis. Computers & Operations Research (in press)
Cohen, P.R.: Empirical Methods for Artificial Intelligence. MIT Press, Cambridge (1995)
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© 2006 Springer-Verlag Berlin Heidelberg
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Balaprakash, P., Birattari, M., Stützle, T., Dorigo, M. (2006). Incremental Local Search in Ant Colony Optimization: Why It Fails for the Quadratic Assignment Problem. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2006. Lecture Notes in Computer Science, vol 4150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839088_14
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DOI: https://doi.org/10.1007/11839088_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38482-3
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