Candidate set strategies for ant colony optimisation

Marcus Randall, James Montgomery

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

8 Citations (Scopus)
68 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 languageEnglish
Title of host publicationAnt Algorithms - 3rd International Workshop, ANTS 2002, Proceedings
EditorsMarco Dorigo , Gianni di Caro, Michael Sampels
PublisherSpringer-Verlag London Ltd.
Pages243-249
Number of pages7
ISBN (Print)9783540457244
DOIs
Publication statusPublished - 1 Jan 2002
Event3rd International Workshop on Ant Algorithms, ANTS 2002 - Brussels, Belgium
Duration: 12 Sep 200214 Sep 2002

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2463
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Ant Algorithms, ANTS 2002
CountryBelgium
CityBrussels
Period12/09/0214/09/02

Fingerprint

Ant colony optimization
Railroad cars
Travelling salesman
Sequencing
Test Problems
Subset
Strategy

Cite this

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 proceedingConference contributionResearchpeer-review

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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)). https://doi.org/10.1007/3-540-45724-0_22