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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/26105
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- Title
- Candidate set strategies for ant colony optimisation
- Author(s)
- Randall, Marcus; Montgomery, James
- Abstract
- Ant Colony Optimisation based solvers systematically scan the set of possible solution elements before choosing a particular one. Hence, the computational time required for each step of the algorithm can be large. One way to overcome this is to limit the number of element choices to a sensible subset, or candidate set. This paper describes some novel generic candidate set strategies and tests these on the travelling salesman and car sequencing problems. The results show that the use of candidate sets helps to find competitive solutions to the test problems in a relatively short amount of time.
- Publication type
- Conference paper
- Source
- Lecture Notes in Computer Science : Ant Algorithms : Proceedings 3rd International Workshop on Ant Algorithms (ANTS 2002), Brussels, Belgium, 12-14 September 2002, Vol. 2463, p. 243-249
- Publication year
- 2002
- Keyword(s)
- ACO; Ant colony optimisation; Candidate set; Car sequencing problems; Travelling salesman problem
- Publisher
- Springer
- ISBN
- 9783540441465
- Publisher URL
- http://dx.doi.org/10.1007/3-540-45724-0_22
- Copyright
- Copyright © Springer Berlin Heidelberg 2002. The author's final draft of this paper is reproduced here in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com.
- Full text
- Peer reviewed
