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

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

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Detail(s)

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization - 12th International Conference, EMO 2023, Proceedings
EditorsMichael Emmerich, André Deutz, Hao Wang, Anna V. Kononova, Boris Naujoks, Ke Li, Kaisa Miettinen, Iryna Yevseyeva
PublisherSpringer, Cham
Pages247-259
ISBN (electronic)9783031272509
ISBN (print)9783031272493
Publication statusPublished - 2023

Publication series

NameLecture Notes in Computer Science
Volume13970
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Conference

Title12th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2023)
LocationHybrid
PlaceNetherlands
CityLeiden
Period20 - 24 March 2023

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.

Citation Format(s)

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 WorksRGC 32 - Refereed conference paper (with host publication)peer-review