Multi-objective optimisation with multiple preferred regions

Md Shahriar Mahbub; Markus Wagner; Luigi Crema

doi:10.1007/978-3-319-51691-2_21

Multi-objective optimisation with multiple preferred regions

Md Shahriar Mahbub, Markus Wagner, Luigi Crema

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

3 Citations (Scopus)

Abstract

The typical goal in multi-objective optimization is to find a set of good and well-distributed solutions. It has become popular to focus on specific regions of the objective space, e.g., due to market demands or personal preferences. In the past, a range of different approaches has been proposed to consider preferences for regions, including reference points and weights. While the former technique requires knowledge over the true set of tradeoffs (and a notion of "closeness") in order to perform well, it is not trivial to encode a non-standard preference for the latter. With this article, we contribute to the set of algorithms that consider preferences. In particular, we propose the easy-to-use concept of “preferred regions” that can be used by laypeople, we explain algorithmic modifications of NSGAII and AGE, and we validate their effectiveness on benchmark problems and on a real-world problem.

Original language	English
Title of host publication	Artificial Life and Computational Intelligence - Third Australasian Conference, ACALCI 2017 Geelong, VIC, Australia, January 31 – February 2, 2017 Proceedings
Editors	Markus Wagner, Xiaodong Li, Tim Hendtlass
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	241-253
Number of pages	13
ISBN (Electronic)	9783319516912
ISBN (Print)	9783319516905
DOIs	https://doi.org/10.1007/978-3-319-51691-2_21
Publication status	Published - 2017
Externally published	Yes
Event	Australasian Conference on Artificial Life and Computational Intelligence 2017 - Deakin University, Geelong, Australia Duration: 31 Jan 2017 → 2 Feb 2017 Conference number: 3rd http://www.acalci.net/2017/ https://link.springer.com/book/10.1007/978-3-319-51691-2 (Springer Proceedings)

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10142
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Australasian Conference on Artificial Life and Computational Intelligence 2017
Abbreviated title	ACALCI 2017
Country/Territory	Australia
City	Geelong
Period	31/01/17 → 2/02/17
Other	ACACLI 2017 is co-located with the Australasian Computer Science Week (ACSW 2017), which will be held at Deakin University's Waterfront Campus, Geelong, which is about 70 kilometers west of Mebourne. 3rd Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017
Internet address	http://www.acalci.net/2017/ https://link.springer.com/book/10.1007/978-3-319-51691-2 (Springer Proceedings)

Keywords

Evolutionary algorithm
Multi-objective optimization
Preference

Access to Document

10.1007/978-3-319-51691-2_21

Cite this

Mahbub, M. S., Wagner, M., & Crema, L. (2017). Multi-objective optimisation with multiple preferred regions. In M. Wagner, X. Li, & T. Hendtlass (Eds.), Artificial Life and Computational Intelligence - Third Australasian Conference, ACALCI 2017 Geelong, VIC, Australia, January 31 – February 2, 2017 Proceedings (pp. 241-253). (Lecture Notes in Computer Science; Vol. 10142). Springer. https://doi.org/10.1007/978-3-319-51691-2_21

Mahbub, Md Shahriar ; Wagner, Markus ; Crema, Luigi. / Multi-objective optimisation with multiple preferred regions. Artificial Life and Computational Intelligence - Third Australasian Conference, ACALCI 2017 Geelong, VIC, Australia, January 31 – February 2, 2017 Proceedings. editor / Markus Wagner ; Xiaodong Li ; Tim Hendtlass. Cham Switzerland : Springer, 2017. pp. 241-253 (Lecture Notes in Computer Science).

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abstract = "The typical goal in multi-objective optimization is to find a set of good and well-distributed solutions. It has become popular to focus on specific regions of the objective space, e.g., due to market demands or personal preferences. In the past, a range of different approaches has been proposed to consider preferences for regions, including reference points and weights. While the former technique requires knowledge over the true set of tradeoffs (and a notion of {"}closeness{"}) in order to perform well, it is not trivial to encode a non-standard preference for the latter. With this article, we contribute to the set of algorithms that consider preferences. In particular, we propose the easy-to-use concept of “preferred regions” that can be used by laypeople, we explain algorithmic modifications of NSGAII and AGE, and we validate their effectiveness on benchmark problems and on a real-world problem.",

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Mahbub, MS, Wagner, M & Crema, L 2017, Multi-objective optimisation with multiple preferred regions. in M Wagner, X Li & T Hendtlass (eds), Artificial Life and Computational Intelligence - Third Australasian Conference, ACALCI 2017 Geelong, VIC, Australia, January 31 – February 2, 2017 Proceedings. Lecture Notes in Computer Science, vol. 10142, Springer, Cham Switzerland, pp. 241-253, Australasian Conference on Artificial Life and Computational Intelligence 2017, Geelong, Victoria, Australia, 31/01/17. https://doi.org/10.1007/978-3-319-51691-2_21

Multi-objective optimisation with multiple preferred regions. / Mahbub, Md Shahriar; Wagner, Markus; Crema, Luigi.
Artificial Life and Computational Intelligence - Third Australasian Conference, ACALCI 2017 Geelong, VIC, Australia, January 31 – February 2, 2017 Proceedings. ed. / Markus Wagner; Xiaodong Li; Tim Hendtlass. Cham Switzerland: Springer, 2017. p. 241-253 (Lecture Notes in Computer Science; Vol. 10142).

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

TY - GEN

T1 - Multi-objective optimisation with multiple preferred regions

AU - Mahbub, Md Shahriar

AU - Wagner, Markus

AU - Crema, Luigi

N1 - Conference code: 3rd

PY - 2017

Y1 - 2017

N2 - The typical goal in multi-objective optimization is to find a set of good and well-distributed solutions. It has become popular to focus on specific regions of the objective space, e.g., due to market demands or personal preferences. In the past, a range of different approaches has been proposed to consider preferences for regions, including reference points and weights. While the former technique requires knowledge over the true set of tradeoffs (and a notion of "closeness") in order to perform well, it is not trivial to encode a non-standard preference for the latter. With this article, we contribute to the set of algorithms that consider preferences. In particular, we propose the easy-to-use concept of “preferred regions” that can be used by laypeople, we explain algorithmic modifications of NSGAII and AGE, and we validate their effectiveness on benchmark problems and on a real-world problem.

AB - The typical goal in multi-objective optimization is to find a set of good and well-distributed solutions. It has become popular to focus on specific regions of the objective space, e.g., due to market demands or personal preferences. In the past, a range of different approaches has been proposed to consider preferences for regions, including reference points and weights. While the former technique requires knowledge over the true set of tradeoffs (and a notion of "closeness") in order to perform well, it is not trivial to encode a non-standard preference for the latter. With this article, we contribute to the set of algorithms that consider preferences. In particular, we propose the easy-to-use concept of “preferred regions” that can be used by laypeople, we explain algorithmic modifications of NSGAII and AGE, and we validate their effectiveness on benchmark problems and on a real-world problem.

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BT - Artificial Life and Computational Intelligence - Third Australasian Conference, ACALCI 2017 Geelong, VIC, Australia, January 31 – February 2, 2017 Proceedings

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A2 - Li, Xiaodong

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PB - Springer

CY - Cham Switzerland

T2 - Australasian Conference on Artificial Life and Computational Intelligence 2017

Y2 - 31 January 2017 through 2 February 2017

ER -

Mahbub MS, Wagner M, Crema L. Multi-objective optimisation with multiple preferred regions. In Wagner M, Li X, Hendtlass T, editors, Artificial Life and Computational Intelligence - Third Australasian Conference, ACALCI 2017 Geelong, VIC, Australia, January 31 – February 2, 2017 Proceedings. Cham Switzerland: Springer. 2017. p. 241-253. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-51691-2_21

Multi-objective optimisation with multiple preferred regions

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Keywords

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