Operations Research Proceedings 2011 pp 355-360 | Cite as
An Enhanced Ant Colony System for the Sequential Ordering Problem
Conference paper
First Online:
Abstract
A well-known Ant Colony System algorithm for the Sequential Ordering Problem is studied to identify its drawbacks. Some criticalities are identified, and an Enhanced Ant Colony System method that tries to overcome them, is proposed. Experimental results show that the enhanced method clearly outperforms the original algorithm and becomes a reference method for the problem under investigation.
Keywords
Local Search Travelling Salesman Problem Precedence Constraint Local Search Procedure Hybrid Particle Swarm Optimization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- 1.D. Anghinolfi, R. Montemanni, M. Paolucci, and L.M. Gambardella. A hybrid particle swarm optimization approach for the sequential ordering problem. Computers and Operations Research, 38(7):1076–1085, 2011.CrossRefGoogle Scholar
- 2.M. Dorigo, V. Maniezzo, and A. Colorni. The Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 26(1):29–41, 1996.CrossRefGoogle Scholar
- 3.L.M. Gambardella and M. Dorigo. An ant colony system hybridized with a new local search for the sequential ordering problem. INFORMS Journal on Computing, 12(3):237–255, 2000.CrossRefGoogle Scholar
- 4.R.Montemanni, D.H. Smith, and L.M. Gambardella. A heuristic manipulation technique for the sequential ordering problem. Computers and Operations Research, 35(12):3931–3944, 2008.CrossRefGoogle Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2012