The dynamic Travelling Thief Problem: benchmarks and performance of evolutionary algorithms

Ragav Sachdeva; Frank Neumann; Markus Wagner

doi:10.1007/978-3-030-63823-8_27

The dynamic Travelling Thief Problem: benchmarks and performance of evolutionary algorithms

Ragav Sachdeva, Frank Neumann, Markus Wagner

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

4 Citations (Scopus)

Abstract

Many real-world optimisation problems involve dynamic and stochastic components. While problems with multiple interacting components are omnipresent in inherently dynamic domains like supply-chain optimisation and logistics, most research on dynamic problems focuses on single-component problems. With this article, we define a number of scenarios based on the Travelling Thief Problem to enable research on the effect of dynamic changes to sub-components. Our investigations of 72 scenarios and seven algorithms show that – depending on the instance, the magnitude of the change, and the algorithms in the portfolio – it is preferable to either restart the optimisation from scratch or to continue with the previously valid solutions.

Original language	English
Title of host publication	Neural Information Processing - 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part V
Editors	Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	220-228
Number of pages	9
ISBN (Electronic)	9783030638238
ISBN (Print)	9783030638221
DOIs	https://doi.org/10.1007/978-3-030-63823-8_27
Publication status	Published - 2020
Externally published	Yes
Event	International Conference on Neural Information Processing 2020 - Bangkok, Thailand Duration: 18 Nov 2020 → 22 Nov 2020 Conference number: 27th https://link.springer.com/book/10.1007/978-3-030-63830-6 (Proceedings)

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	1333
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	International Conference on Neural Information Processing 2020
Abbreviated title	ICONIP 2020
Country/Territory	Thailand
City	Bangkok
Period	18/11/20 → 22/11/20
Internet address	https://link.springer.com/book/10.1007/978-3-030-63830-6 (Proceedings)

Keywords

Dynamic optimisation
Multi-component problems

Access to Document

10.1007/978-3-030-63823-8_27

Cite this

Sachdeva, R., Neumann, F., & Wagner, M. (2020). The dynamic Travelling Thief Problem: benchmarks and performance of evolutionary algorithms. In H. Yang, K. Pasupa, A. C.-S. Leung, J. T. Kwok, J. H. Chan, & I. King (Eds.), Neural Information Processing - 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part V (pp. 220-228). (Communications in Computer and Information Science; Vol. 1333). Springer. https://doi.org/10.1007/978-3-030-63823-8_27

Sachdeva, Ragav ; Neumann, Frank ; Wagner, Markus. / The dynamic Travelling Thief Problem : benchmarks and performance of evolutionary algorithms. Neural Information Processing - 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part V. editor / Haiqin Yang ; Kitsuchart Pasupa ; Andrew Chi-Sing Leung ; James T. Kwok ; Jonathan H. Chan ; Irwin King. Cham Switzerland : Springer, 2020. pp. 220-228 (Communications in Computer and Information Science).

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title = "The dynamic Travelling Thief Problem: benchmarks and performance of evolutionary algorithms",

abstract = "Many real-world optimisation problems involve dynamic and stochastic components. While problems with multiple interacting components are omnipresent in inherently dynamic domains like supply-chain optimisation and logistics, most research on dynamic problems focuses on single-component problems. With this article, we define a number of scenarios based on the Travelling Thief Problem to enable research on the effect of dynamic changes to sub-components. Our investigations of 72 scenarios and seven algorithms show that – depending on the instance, the magnitude of the change, and the algorithms in the portfolio – it is preferable to either restart the optimisation from scratch or to continue with the previously valid solutions.",

keywords = "Dynamic optimisation, Multi-component problems",

author = "Ragav Sachdeva and Frank Neumann and Markus Wagner",

note = "Funding Information: Acknowledgment. This work has been supported by the Australian Research Council through grants DP160102401 and DP200102364. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; International Conference on Neural Information Processing 2020, ICONIP 2020 ; Conference date: 18-11-2020 Through 22-11-2020",

year = "2020",

doi = "10.1007/978-3-030-63823-8_27",

language = "English",

isbn = "9783030638221",

series = "Communications in Computer and Information Science",

publisher = "Springer",

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editor = "Haiqin Yang and Kitsuchart Pasupa and Leung, {Andrew Chi-Sing} and Kwok, {James T.} and Chan, {Jonathan H.} and Irwin King",

booktitle = "Neural Information Processing - 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part V",

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Sachdeva, R, Neumann, F & Wagner, M 2020, The dynamic Travelling Thief Problem: benchmarks and performance of evolutionary algorithms. in H Yang, K Pasupa, AC-S Leung, JT Kwok, JH Chan & I King (eds), Neural Information Processing - 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part V. Communications in Computer and Information Science, vol. 1333, Springer, Cham Switzerland, pp. 220-228, International Conference on Neural Information Processing 2020, Bangkok, Thailand, 18/11/20. https://doi.org/10.1007/978-3-030-63823-8_27

The dynamic Travelling Thief Problem: benchmarks and performance of evolutionary algorithms. / Sachdeva, Ragav; Neumann, Frank; Wagner, Markus.
Neural Information Processing - 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part V. ed. / Haiqin Yang; Kitsuchart Pasupa; Andrew Chi-Sing Leung; James T. Kwok; Jonathan H. Chan; Irwin King. Cham Switzerland: Springer, 2020. p. 220-228 (Communications in Computer and Information Science; Vol. 1333).

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

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AB - Many real-world optimisation problems involve dynamic and stochastic components. While problems with multiple interacting components are omnipresent in inherently dynamic domains like supply-chain optimisation and logistics, most research on dynamic problems focuses on single-component problems. With this article, we define a number of scenarios based on the Travelling Thief Problem to enable research on the effect of dynamic changes to sub-components. Our investigations of 72 scenarios and seven algorithms show that – depending on the instance, the magnitude of the change, and the algorithms in the portfolio – it is preferable to either restart the optimisation from scratch or to continue with the previously valid solutions.

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Sachdeva R, Neumann F, Wagner M. The dynamic Travelling Thief Problem: benchmarks and performance of evolutionary algorithms. In Yang H, Pasupa K, Leung ACS, Kwok JT, Chan JH, King I, editors, Neural Information Processing - 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part V. Cham Switzerland: Springer. 2020. p. 220-228. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-63823-8_27

The dynamic Travelling Thief Problem: benchmarks and performance of evolutionary algorithms

Abstract

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Keywords

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