Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables

Ping Guo; Qingfu Zhang; Xi Lin

doi:10.1007/978-3-031-27250-9_18

Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables

Ping Guo^*
, Qingfu Zhang
, Xi Lin

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

4 Citations (Scopus)

Abstract

In many real-world applications, the Pareto Set (PS) of a continuous multiobjective optimization problem can be a piecewise continuous manifold. A decision maker may want to find a solution set that approximates a small part of the PS and requires the solutions in this set share some similarities. This paper makes a first attempt to address this issue. We first develop a performance metric that considers both optimality and variable sharing. Then we design an algorithm for finding the model that minimizes the metric to meet the user’s requirements. Experimental results illustrate that we can obtain a linear model that approximates the mapping from the preference vectors to solutions in a local area well. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings
Editors	Michael Emmerich, André Deutz, Hao Wang, Anna V. Kononova, Boris Naujoks, Ke Li, Kaisa Miettinen, Iryna Yevseyeva
Publisher	Springer, Cham
Pages	247-259
ISBN (Electronic)	9783031272509
ISBN (Print)	9783031272493
DOIs	https://doi.org/10.1007/978-3-031-27250-9_18
Publication status	Published - 2023
Event	12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023) - Hybrid, Leiden, Netherlands Duration: 20 Mar 2023 → 24 Mar 2023 https://emo2023.liacs.leidenuniv.nl/

Publication series

Name	Lecture Notes in Computer Science
Volume	13970
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023)
Place	Netherlands
City	Leiden
Period	20/03/23 → 24/03/23
Internet address	https://emo2023.liacs.leidenuniv.nl/

Funding

This work is supported by the General Research Fund (GRF) grant (CityU 11215622) from the Research Grants Council of Hong Kong, China.

RGC Funding Information

RGC-funded

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10.1007/978-3-031-27250-9_18

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Guo, P., Zhang, Q., & Lin, X. (2023). Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables. In M. Emmerich, A. Deutz, H. Wang, A. V. Kononova, B. Naujoks, K. Li, K. Miettinen, & I. Yevseyeva (Eds.), Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings (pp. 247-259). (Lecture Notes in Computer Science; Vol. 13970). Springer, Cham. https://doi.org/10.1007/978-3-031-27250-9_18

Guo, Ping ; Zhang, Qingfu ; Lin, Xi. / Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables. Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings. editor / Michael Emmerich ; André Deutz ; Hao Wang ; Anna V. Kononova ; Boris Naujoks ; Ke Li ; Kaisa Miettinen ; Iryna Yevseyeva. Springer, Cham, 2023. pp. 247-259 (Lecture Notes in Computer Science).

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title = "Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables",

abstract = "In many real-world applications, the Pareto Set (PS) of a continuous multiobjective optimization problem can be a piecewise continuous manifold. A decision maker may want to find a solution set that approximates a small part of the PS and requires the solutions in this set share some similarities. This paper makes a first attempt to address this issue. We first develop a performance metric that considers both optimality and variable sharing. Then we design an algorithm for finding the model that minimizes the metric to meet the user{\textquoteright}s requirements. Experimental results illustrate that we can obtain a linear model that approximates the mapping from the preference vectors to solutions in a local area well. {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",

author = "Ping Guo and Qingfu Zhang and Xi Lin",

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doi = "10.1007/978-3-031-27250-9\_18",

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booktitle = "Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings",

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Guo, P , Zhang, Q & Lin, X 2023, Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables. in M Emmerich, A Deutz, H Wang, AV Kononova, B Naujoks, K Li, K Miettinen & I Yevseyeva (eds), Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings. Lecture Notes in Computer Science, vol. 13970, Springer, Cham, pp. 247-259, 12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023), Leiden, Netherlands, 20/03/23. https://doi.org/10.1007/978-3-031-27250-9_18

Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables. / Guo, Ping ; Zhang, Qingfu ; Lin, Xi.
Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings. ed. / Michael Emmerich; André Deutz; Hao Wang; Anna V. Kononova; Boris Naujoks; Ke Li; Kaisa Miettinen; Iryna Yevseyeva. Springer, Cham, 2023. p. 247-259 (Lecture Notes in Computer Science; Vol. 13970).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Guo, Ping

AU - Zhang, Qingfu

AU - Lin, Xi

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AB - In many real-world applications, the Pareto Set (PS) of a continuous multiobjective optimization problem can be a piecewise continuous manifold. A decision maker may want to find a solution set that approximates a small part of the PS and requires the solutions in this set share some similarities. This paper makes a first attempt to address this issue. We first develop a performance metric that considers both optimality and variable sharing. Then we design an algorithm for finding the model that minimizes the metric to meet the user’s requirements. Experimental results illustrate that we can obtain a linear model that approximates the mapping from the preference vectors to solutions in a local area well. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9783031272493

T3 - Lecture Notes in Computer Science

SP - 247

EP - 259

BT - Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings

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A2 - Li, Ke

A2 - Miettinen, Kaisa

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PB - Springer, Cham

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Y2 - 20 March 2023 through 24 March 2023

ER -

Guo P , Zhang Q , Lin X. Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables. In Emmerich M, Deutz A, Wang H, Kononova AV, Naujoks B, Li K, Miettinen K, Yevseyeva I, editors, Evolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings. Springer, Cham. 2023. p. 247-259. (Lecture Notes in Computer Science). Epub 2023 Mar 9. doi: 10.1007/978-3-031-27250-9_18

Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables

Abstract

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GRF: Few for Many: A Non-Pareto Approach for Many Objective Optimization

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Approximation of a Pareto Set Segment Using a Linear Model with Sharing Variables

Abstract

Publication series

Conference

Funding

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

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GRF: Few for Many: A Non-Pareto Approach for Many Objective Optimization

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