Abstract
Stochastic solvers are researched primarily with the goal of providing ‘black box’ optimisation approaches for situations where the optimisation problem is too complex to model and therefore impossible to solve using a deterministic approach. Sometimes, however, problems or their instances have characteristics which interact with the solver in undocumented and unpredictable ways. This paper reviews some pertinent examples in the literature and provides an experiment which demonstrates that ant colony optimisation has arcane mechanisms which are partly responsible for results which are currently attributed to the pheromone-based learning.
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
Barr, R.S., Golden, B.L., Kelly, J.P., Resende, M.G.C., Stewart, W.R.: Designing and Reporting on Computational Experiments with Heuristic Methods. Journal of Heuristics 1, 9–32 (1995)
Cobb, H.G.: An Investigation into the Use of Hypermutation as an Adaptive Operator in Genetic Algorithms Having Continuous Time-Dependent Nonstationary Environments. Technical Report, Naval Research Laboratory, Washington (1990)
Dorigo, M., Gambardella, L.: Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1, 53–66 (1997)
Dorigo, M., Stützle, T.: Ant Colony Optimization. The MIT Press, Cambridge, MA (2004)
Fisher, D.S.: Dynamics and domain walls: Is the ’landscape paradigm’ instructive? Physica D 107, 204–217 (1997)
Grefenstette, J.J.: Genetic algorithms for changing environments. Parallel Problem Solving from Nature 2, 137–144 (1992)
Janson, S., Middendorf, M.: On Trajectories of Particles in PSO. In: Proceedings of the 2007 IEEE Swarm Intelligence Symposium (2007)
Lin, S., Kernighan, B.W.: An effective heuristic algorithm for the traveling salesman problem. Operations Research 21, 498–516 (1973)
Merkle, D.: Ameisenalgorithmen – Optimierung und Modellierung. PhD thesis, Institut für Angewandte Informatik und Formale Beschreibungsverfahren, Universität Karlsruhe (TH) (in German) (2002)
Mitchell, M., Holland, J.H., Forrest, S.: When Will a Genetic Algorithm Outperform Hill Climbing? Advances in Neural Information Processing Systems 6 (1994)
http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/
Younes, A., Calamai, P., Basir, O.: Generalized Benchmark Generation for Dynamic Combinatorial Problems. In: Proceedings of the 2005 workshops on Genetic and evolutionary computation, pp. 25–31 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Moser, I. (2007). Concealed Contributors to Result Quality — The Search Process of Ant Colony System. In: Randall, M., Abbass, H.A., Wiles, J. (eds) Progress in Artificial Life. ACAL 2007. Lecture Notes in Computer Science(), vol 4828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76931-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-76931-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76930-9
Online ISBN: 978-3-540-76931-6
eBook Packages: Computer ScienceComputer Science (R0)