Candidate set strategies for ant colony optimisation

Marcus Randall; James Montgomery

doi:10.1007/3-540-45724-0_22

Candidate set strategies for ant colony optimisation

Marcus Randall, James Montgomery

Bond Business School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

14 Citations (Scopus)

270 Downloads (Pure)

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.

Original language	English
Title of host publication	Ant Algorithms - 3rd International Workshop, ANTS 2002, Proceedings
Editors	Marco Dorigo , Gianni di Caro, Michael Sampels
Publisher	Springer-Verlag London Ltd.
Pages	243-249
Number of pages	7
ISBN (Print)	9783540457244
DOIs	https://doi.org/10.1007/3-540-45724-0_22
Publication status	Published - 1 Jan 2002
Event	3rd International Workshop on Ant Algorithms, ANTS 2002 - Brussels, Belgium Duration: 12 Sept 2002 → 14 Sept 2002

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2463
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Workshop on Ant Algorithms, ANTS 2002
Country/Territory	Belgium
City	Brussels
Period	12/09/02 → 14/09/02

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10.1007/3-540-45724-0_22

Candidate set strategies for ant colony optimisationAccepted author manuscript, 150 KBLicence: Unspecified

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Randall, M., & Montgomery, J. (2002). Candidate set strategies for ant colony optimisation. In M. Dorigo , G. di Caro, & M. Sampels (Eds.), Ant Algorithms - 3rd International Workshop, ANTS 2002, Proceedings (pp. 243-249). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2463). Springer-Verlag London Ltd.. https://doi.org/10.1007/3-540-45724-0_22

Randall, Marcus ; Montgomery, James. / Candidate set strategies for ant colony optimisation. Ant Algorithms - 3rd International Workshop, ANTS 2002, Proceedings. editor / Marco Dorigo ; Gianni di Caro ; Michael Sampels. Springer-Verlag London Ltd., 2002. pp. 243-249 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Randall, M & Montgomery, J 2002, Candidate set strategies for ant colony optimisation. in M Dorigo , G di Caro & M Sampels (eds), Ant Algorithms - 3rd International Workshop, ANTS 2002, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2463, Springer-Verlag London Ltd., pp. 243-249, 3rd International Workshop on Ant Algorithms, ANTS 2002, Brussels, Belgium, 12/09/02. https://doi.org/10.1007/3-540-45724-0_22

Candidate set strategies for ant colony optimisation. / Randall, Marcus; Montgomery, James.
Ant Algorithms - 3rd International Workshop, ANTS 2002, Proceedings. ed. / Marco Dorigo ; Gianni di Caro; Michael Sampels. Springer-Verlag London Ltd., 2002. p. 243-249 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2463).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

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AB - 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.

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Randall M, Montgomery J. Candidate set strategies for ant colony optimisation. In Dorigo M, di Caro G, Sampels M, editors, Ant Algorithms - 3rd International Workshop, ANTS 2002, Proceedings. Springer-Verlag London Ltd. 2002. p. 243-249. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45724-0_22

Candidate set strategies for ant colony optimisation

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