Parameter prediction based on features of evolved instances for ant colony optimization and the Traveling Salesperson problem

Samadhi Nallaperuma; Markus Wagner; Frank Neumann

doi:10.1007/978-3-319-10762-2_10

Parameter prediction based on features of evolved instances for ant colony optimization and the Traveling Salesperson problem

Samadhi Nallaperuma, Markus Wagner, Frank Neumann

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

10 Citations (Scopus)

Abstract

Ant colony optimization performs verywell onmany hard optimization problems, even though no good worst case guarantee can be given. Understanding the reasons for the performance and the influence of its different parameter settings has become an interesting problem. In this paper, we build a parameter prediction model for the Traveling Salesperson problem based on features of evolved instances. The two considered parameters are the importance of the pheromone values and of the heuristic information. Based on the features of the evolved instances, we successfully predict the best parameter setting for a wide range of instances taken from TSPLIB.

Original language	English
Title of host publication	Parallel Problem Solving from Nature -- PPSN XIII
Subtitle of host publication	13th International Conference, Ljubljana, Slovenia, September 13-17,2014, Proceedings
Publisher	Springer
Pages	100-109
Number of pages	10
Volume	8672
DOIs	https://doi.org/10.1007/978-3-319-10762-2_10
Publication status	Published - 2014
Externally published	Yes
Event	Parallel Problem Solving from Nature 2014 - Ljubljana, Slovenia Duration: 13 Sept 2014 → 17 Sept 2014 Conference number: 13th https://link.springer.com/book/10.1007/978-3-319-10762-2

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)	0302-9743

Conference

Conference	Parallel Problem Solving from Nature 2014
Abbreviated title	PPSN XIII
Country/Territory	Slovenia
City	Ljubljana
Period	13/09/14 → 17/09/14
Internet address	https://link.springer.com/book/10.1007/978-3-319-10762-2

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10.1007/978-3-319-10762-2_10

Cite this

Nallaperuma, S., Wagner, M., & Neumann, F. (2014). Parameter prediction based on features of evolved instances for ant colony optimization and the Traveling Salesperson problem. In Parallel Problem Solving from Nature -- PPSN XIII: 13th International Conference, Ljubljana, Slovenia, September 13-17,2014, Proceedings (Vol. 8672, pp. 100-109). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Springer. https://doi.org/10.1007/978-3-319-10762-2_10

Nallaperuma, Samadhi ; Wagner, Markus ; Neumann, Frank. / Parameter prediction based on features of evolved instances for ant colony optimization and the Traveling Salesperson problem. Parallel Problem Solving from Nature -- PPSN XIII: 13th International Conference, Ljubljana, Slovenia, September 13-17,2014, Proceedings. Vol. 8672 Springer, 2014. pp. 100-109 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Parameter prediction based on features of evolved instances for ant colony optimization and the Traveling Salesperson problem",

abstract = "Ant colony optimization performs verywell onmany hard optimization problems, even though no good worst case guarantee can be given. Understanding the reasons for the performance and the influence of its different parameter settings has become an interesting problem. In this paper, we build a parameter prediction model for the Traveling Salesperson problem based on features of evolved instances. The two considered parameters are the importance of the pheromone values and of the heuristic information. Based on the features of the evolved instances, we successfully predict the best parameter setting for a wide range of instances taken from TSPLIB.",

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

note = "Funding Information: Acknowledgements. We thank Bernd Bischl for early discussions, Heike Trautmann and Olaf Mersmann for the feedback on the preliminary version of this research. This research has been supported by the Australian Research Council (ARC) under grant agreement DP140103400. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; Parallel Problem Solving from Nature 2014, PPSN XIII ; Conference date: 13-09-2014 Through 17-09-2014",

year = "2014",

doi = "10.1007/978-3-319-10762-2_10",

language = "English",

volume = "8672",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "100--109",

booktitle = "Parallel Problem Solving from Nature -- PPSN XIII",

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Nallaperuma, S, Wagner, M & Neumann, F 2014, Parameter prediction based on features of evolved instances for ant colony optimization and the Traveling Salesperson problem. in Parallel Problem Solving from Nature -- PPSN XIII: 13th International Conference, Ljubljana, Slovenia, September 13-17,2014, Proceedings. vol. 8672, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, pp. 100-109, Parallel Problem Solving from Nature 2014, Ljubljana, Slovenia, 13/09/14. https://doi.org/10.1007/978-3-319-10762-2_10

Parameter prediction based on features of evolved instances for ant colony optimization and the Traveling Salesperson problem. / Nallaperuma, Samadhi; Wagner, Markus; Neumann, Frank.
Parallel Problem Solving from Nature -- PPSN XIII: 13th International Conference, Ljubljana, Slovenia, September 13-17,2014, Proceedings. Vol. 8672 Springer, 2014. p. 100-109 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

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AB - Ant colony optimization performs verywell onmany hard optimization problems, even though no good worst case guarantee can be given. Understanding the reasons for the performance and the influence of its different parameter settings has become an interesting problem. In this paper, we build a parameter prediction model for the Traveling Salesperson problem based on features of evolved instances. The two considered parameters are the importance of the pheromone values and of the heuristic information. Based on the features of the evolved instances, we successfully predict the best parameter setting for a wide range of instances taken from TSPLIB.

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Nallaperuma S, Wagner M, Neumann F. Parameter prediction based on features of evolved instances for ant colony optimization and the Traveling Salesperson problem. In Parallel Problem Solving from Nature -- PPSN XIII: 13th International Conference, Ljubljana, Slovenia, September 13-17,2014, Proceedings. Vol. 8672. Springer. 2014. p. 100-109. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-10762-2_10

Parameter prediction based on features of evolved instances for ant colony optimization and the Traveling Salesperson problem

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