Near parameter free ant colony optimisation

Marcus Randall

doi:10.1007/978-3-540-28646-2_37

Near parameter free ant colony optimisation

Marcus Randall^*

^*Corresponding author for this work

Bond Business School

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

35 Citations (Scopus)

224 Downloads (Pure)

Abstract

Ant colony optimisation, like all other meta-heuristic search processes, requires a set of parameters in order to solve combinatorial problems. These parameters are often tuned by hand by the researcher to a set that seems to work well for the problem under study or a standard set from the literature. However, it is possible to integrate a parameter search process within the running of the meta-heuristic without incurring an undue computational overhead. In this paper, ant colony optimisation is used to evolve suitable parameter values (using its own optimisation processes) while it is solving combinatorial problems. The results reveal for the travelling salesman and quadratic assignment problems that the use of the augmented solver generally performs well against one that uses a standard set of parameter values. This is attributed to the fact that parameter values suitable for the particular problem instance can be automatically derived and varied throughout the search process.

Original language	English
Title of host publication	Ant Colony Optimization and Swarm Intelligence
Subtitle of host publication	ANTS 2004
Editors	M Dorigo, M Birattari, C Blum, L M Gambardella, F Mondada, T Stutzle
Place of Publication	Heidelberg
Publisher	Springer
Pages	374-381
Number of pages	8
Volume	3172 LNCS
ISBN (Print)	3540226729, 9783540226727
DOIs	https://doi.org/10.1007/978-3-540-28646-2_37
Publication status	Published - 2004
Event	International Workshop on Ant Colony Optimization and Swarm Intelligence - Brussels, Belgium Duration: 5 Sept 2004 → 8 Sept 2004 Conference number: 4th http://iridia.ulb.ac.be/ants/ants2004/ http://iridia.ulb.ac.be/ants/ants2004/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3172 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	International Workshop on Ant Colony Optimization and Swarm Intelligence
Abbreviated title	ANTS 2004
Country/Territory	Belgium
City	Brussels
Period	5/09/04 → 8/09/04
Internet address	http://iridia.ulb.ac.be/ants/ants2004/ http://iridia.ulb.ac.be/ants/ants2004/

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10.1007/978-3-540-28646-2_37

Near parameter free ant colony optimisationAccepted author manuscript, 148 KBLicence: Unspecified

Cite this

Randall, M. (2004). Near parameter free ant colony optimisation. In M. Dorigo, M. Birattari, C. Blum, L. M. Gambardella, F. Mondada, & T. Stutzle (Eds.), Ant Colony Optimization and Swarm Intelligence : ANTS 2004 (Vol. 3172 LNCS, pp. 374-381). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3172 LNCS). Springer. https://doi.org/10.1007/978-3-540-28646-2_37

Randall, Marcus. / Near parameter free ant colony optimisation. Ant Colony Optimization and Swarm Intelligence : ANTS 2004. editor / M Dorigo ; M Birattari ; C Blum ; L M Gambardella ; F Mondada ; T Stutzle. Vol. 3172 LNCS Heidelberg : Springer, 2004. pp. 374-381 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Ant colony optimisation, like all other meta-heuristic search processes, requires a set of parameters in order to solve combinatorial problems. These parameters are often tuned by hand by the researcher to a set that seems to work well for the problem under study or a standard set from the literature. However, it is possible to integrate a parameter search process within the running of the meta-heuristic without incurring an undue computational overhead. In this paper, ant colony optimisation is used to evolve suitable parameter values (using its own optimisation processes) while it is solving combinatorial problems. The results reveal for the travelling salesman and quadratic assignment problems that the use of the augmented solver generally performs well against one that uses a standard set of parameter values. This is attributed to the fact that parameter values suitable for the particular problem instance can be automatically derived and varied throughout the search process.",

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Randall, M 2004, Near parameter free ant colony optimisation. in M Dorigo, M Birattari, C Blum, LM Gambardella, F Mondada & T Stutzle (eds), Ant Colony Optimization and Swarm Intelligence : ANTS 2004. vol. 3172 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3172 LNCS, Springer, Heidelberg, pp. 374-381, International Workshop on Ant Colony Optimization and Swarm Intelligence, Brussels, Belgium, 5/09/04. https://doi.org/10.1007/978-3-540-28646-2_37

Near parameter free ant colony optimisation. / Randall, Marcus.
Ant Colony Optimization and Swarm Intelligence : ANTS 2004. ed. / M Dorigo; M Birattari; C Blum; L M Gambardella; F Mondada; T Stutzle. Vol. 3172 LNCS Heidelberg: Springer, 2004. p. 374-381 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3172 LNCS).

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

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AB - Ant colony optimisation, like all other meta-heuristic search processes, requires a set of parameters in order to solve combinatorial problems. These parameters are often tuned by hand by the researcher to a set that seems to work well for the problem under study or a standard set from the literature. However, it is possible to integrate a parameter search process within the running of the meta-heuristic without incurring an undue computational overhead. In this paper, ant colony optimisation is used to evolve suitable parameter values (using its own optimisation processes) while it is solving combinatorial problems. The results reveal for the travelling salesman and quadratic assignment problems that the use of the augmented solver generally performs well against one that uses a standard set of parameter values. This is attributed to the fact that parameter values suitable for the particular problem instance can be automatically derived and varied throughout the search process.

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Randall M. Near parameter free ant colony optimisation. In Dorigo M, Birattari M, Blum C, Gambardella LM, Mondada F, Stutzle T, editors, Ant Colony Optimization and Swarm Intelligence : ANTS 2004. Vol. 3172 LNCS. Heidelberg: Springer. 2004. p. 374-381. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-28646-2_37

Near parameter free ant colony optimisation

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