Abstract
Ant colony optimisation is a constructive metaheuristic that successively builds solutions from problem-specific components. A parameterised model known as pheromone—an analogue of the trail pheromones used by real ants—is used to learn which components should be combined to produce good solutions. In the majority of the algorithm’s applications a single parameter from the model is used to influence the selection of a single component to add to a solution. Such a model can be described as first order. Higher order models describe relationships between several components in a solution, and may arise either by contriving a model that describes subsets of components from a first order model or because the characteristics of solutions modelled naturally relate subsets of components. This paper introduces a simple framework to describe the application of higher order models as a tool to understanding common features of existing applications. The framework also serves as an introduction to those new to the use of such models. The utility of higher order models is discussed with reference to empirical results in the literature.
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
Zlochin, M., Dorigo, M.: Model-based search for combinatorial optimization: A comparative study. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 651–662. Springer, Heidelberg (2002)
Montgomery, J., Randall, M., Hendtlass, T.: Automated selection of appropriate pheromone representations in ant colony optimisation. Artificial Life 11, 269–291 (2005)
Blum, C.: Theoretical and practical aspects of ant colony optimization. Dissertations in Artificial Intelligence, vol. 282. IOS Press, Nieuwe Hemweg (2004)
Blum, C., Sampels, M.: Ant colony optimization for FOP shop scheduling: A case study on different pheromone representations. In: 2002 Congress on Evolutionary Computation, pp. 1558–1563 (2002)
Montgomery, E.J.: Solution Biases and Pheromone Representation Selection in Ant Colony Optimisation. Ph.D thesis, Bond University (2005)
Stützle, T., Hoos, H.: \(\mathcal{MAX-MIN}\) ant system. Future Gen. Comp. Sys. 16, 889–914 (2000)
Roli, A., Blum, C., Dorigo, M.: ACO for maximal constraint satisfaction problems. In: 4th Metaheuristics International Conference, Porto, Portugal, pp. 187–192 (2001)
Solnon, C.: Ants can solve constraint satisfaction problems. IEEE Trans. Evol. Comput. 6 (2002)
Fenet, S., Solnon, C.: Searching for maximum cliques with ant colony optimization. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 236–245. Springer, Heidelberg (2003)
Blum, C.: Ant colony optimization for the edge-weighted k-cardinality tree problem. In: Langdon, W. (ed.) Genetic and Evolutionary Computation Conference, pp. 27–34. Morgan Kaufmann Publishers, San Francisco (2002)
Costa, D., Hertz, A.: Ants can colour graphs. J. Oper. Res. Soc. 48, 295–305 (1997)
Schoofs, L., Naudts, B.: Solving CSPs with ant colonies. In: Abstract Proceedings of ANTS 2000, Brussels, Belgium (2000)
Ducatelle, F., Levine, J.: Ant colony optimisation and local search for bin packing and cutting stock problems. J. Oper. Res. Soc. 55, 705–716 (2004)
Socha, K., Knowles, J.D., Sampels, M.: A \(\cal{MAX-MIN}\) ant system for the university course timetabling problem. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 1–13. Springer, Heidelberg (2002)
Blum, C., Sampels, M.: An ant colony optimization algorithm for shop scheduling problems. J. Math. Model. Algorithms 3, 285–308 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Montgomery, J. (2006). Higher Order Pheromone Models in Ant Colony Optimisation. 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_42
Download citation
DOI: https://doi.org/10.1007/11839088_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38482-3
Online ISBN: 978-3-540-38483-0
eBook Packages: Computer ScienceComputer Science (R0)