Ant colony optimisation and the traveling salesperson problem - Hardness, features and parameter settings

Samadhi Nallaperuma; Markus Wagner; Frank Neumann

doi:10.1145/2464576.2464581

Ant colony optimisation and the traveling salesperson problem - Hardness, features and parameter settings

Samadhi Nallaperuma, Markus Wagner, Frank Neumann

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

6 Citations (Scopus)

Abstract

Our study on ant colony optimization (ACO) and the Travelling Salesperson Problem (TSP) attempts to understand the effect of parameters and instance features on performance using statistical analysis of the hard, easy and average problem instances for an algorithm instance.

Original language	English
Title of host publication	GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion
Publisher	Association for Computing Machinery (ACM)
Pages	13-14
Number of pages	2
ISBN (Print)	9781450319645
DOIs	https://doi.org/10.1145/2464576.2464581
Publication status	Published - 2013
Externally published	Yes
Event	The Genetic and Evolutionary Computation Conference 2013 - Amsterdam, Netherlands Duration: 6 Jul 2013 → 10 Jul 2013 Conference number: 15th https://dl.acm.org/doi/proceedings/10.1145/2463372 (Proceedings)

Conference

Conference	The Genetic and Evolutionary Computation Conference 2013
Abbreviated title	GECCO 2013
Country/Territory	Netherlands
City	Amsterdam
Period	6/07/13 → 10/07/13
Internet address	https://dl.acm.org/doi/proceedings/10.1145/2463372 (Proceedings)

Keywords

Ant colony optimisation
Features
Parameters
Traveling salesperson problem

Access to Document

10.1145/2464576.2464581

Cite this

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title = "Ant colony optimisation and the traveling salesperson problem - Hardness, features and parameter settings",

abstract = "Our study on ant colony optimization (ACO) and the Travelling Salesperson Problem (TSP) attempts to understand the effect of parameters and instance features on performance using statistical analysis of the hard, easy and average problem instances for an algorithm instance.",

keywords = "Ant colony optimisation, Features, Parameters, Traveling salesperson problem",

author = "Samadhi Nallaperuma and Markus Wagner and Frank Neumann",

year = "2013",

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language = "English",

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pages = "13--14",

booktitle = "GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

note = "The Genetic and Evolutionary Computation Conference 2013, GECCO 2013 ; Conference date: 06-07-2013 Through 10-07-2013",

url = "https://dl.acm.org/doi/proceedings/10.1145/2463372",

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Nallaperuma, S, Wagner, M & Neumann, F 2013, Ant colony optimisation and the traveling salesperson problem - Hardness, features and parameter settings. in GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM), pp. 13-14, The Genetic and Evolutionary Computation Conference 2013, Amsterdam, Netherlands, 6/07/13. https://doi.org/10.1145/2464576.2464581

Ant colony optimisation and the traveling salesperson problem - Hardness, features and parameter settings. / Nallaperuma, Samadhi; Wagner, Markus; Neumann, Frank.
GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM), 2013. p. 13-14.

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

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T1 - Ant colony optimisation and the traveling salesperson problem - Hardness, features and parameter settings

AU - Nallaperuma, Samadhi

AU - Wagner, Markus

AU - Neumann, Frank

N1 - Conference code: 15th

PY - 2013

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N2 - Our study on ant colony optimization (ACO) and the Travelling Salesperson Problem (TSP) attempts to understand the effect of parameters and instance features on performance using statistical analysis of the hard, easy and average problem instances for an algorithm instance.

AB - Our study on ant colony optimization (ACO) and the Travelling Salesperson Problem (TSP) attempts to understand the effect of parameters and instance features on performance using statistical analysis of the hard, easy and average problem instances for an algorithm instance.

KW - Ant colony optimisation

KW - Features

KW - Parameters

KW - Traveling salesperson problem

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U2 - 10.1145/2464576.2464581

DO - 10.1145/2464576.2464581

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EP - 14

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T2 - The Genetic and Evolutionary Computation Conference 2013

Y2 - 6 July 2013 through 10 July 2013

ER -

Ant colony optimisation and the traveling salesperson problem - Hardness, features and parameter settings

Abstract

Conference

Keywords

Access to Document

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