Abstract
ACO algorithms iteratively build solutions to an optimization problem. The solution construction process is guided by pheromone trails which represents a mechanism of adaptation that allows to bias the sampling of new solutions toward promising regions of the search space. Additionally, the bias of the search is influenced by problem dependent heuristic information. In this work we describe an ACO algorithm that incorporates principles of Tabu Search (TS) for the solution construction process. These concepts specifically address the way that TS uses the history of the search to avoid visiting solutions already analyzed. We consider the Quadratic Assignment Problem (QAP) as a case-study, since this problem was also tackled in a closely related research to ours, the one on the usage of external memory in ACO algorithms. The performance of the proposed algorithm is assessed by considering a well-known set of instances of QAP.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Acan, A.: An external memory implementation in ant colony optimization. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 73–84. Springer, Heidelberg (2004)
Acan, A.: An external partial permutations memory for ant colony optimization. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 1–11. Springer, Heidelberg (2005)
Cela, E.: The Quadratic Assignment Problem: Theory and Algorithms. Kluwer Academic Publishers, Dordrecht (1998)
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)
Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers, Norwell (1997)
Sahni, S., Gonzalez, T.: P-complete approximation problems. Journal of the ACM 23(3), 555–565 (1976)
Stützle, T.: \(\mathcal{MAX-MIN}\) for the quadratic assignment problem. Technical report, AIDA-97-4, FG Intellektik, FB Informatik, TU Darmstadt, Germany (1997)
Stützle, T., Fernandes, S.: New benchmark instances for the QAP and the experimental analysis of algorithms. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2004. LNCS, vol. 3004, pp. 199–209. Springer, Heidelberg (2004)
Stützle, T., Hoos, H.: mathcalMAX-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)
Tsutsui, S.: cAS: Ant colony optimization with cunning ants. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 162–171. Springer, Heidelberg (2006)
Wiesemann, W., Stützle, T.: Iterated ants: An experimental study for the quadratic assignment problem. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 179–190. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arito, F., Leguizamón, G. (2009). Incorporating Tabu Search Principles into ACO Algorithms. In: Blesa, M.J., Blum, C., Di Gaspero, L., Roli, A., Sampels, M., Schaerf, A. (eds) Hybrid Metaheuristics. HM 2009. Lecture Notes in Computer Science, vol 5818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04918-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-04918-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04917-0
Online ISBN: 978-3-642-04918-7
eBook Packages: Computer ScienceComputer Science (R0)