What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization

Conference Paper (2023)
Author(s)

Z. MS Osika (TU Delft - Interactive Intelligence)

J. Salazar (TU Delft - Policy Analysis)

Diederik M. Roijers (Vrije Universiteit Brussel, Gemeente Amsterdam)

FA Oliehoek (TU Delft - Interactive Intelligence)

Pradeep Murukannaiah (TU Delft - Interactive Intelligence)

Research Group
Policy Analysis
Copyright
© 2023 Z. MS Osika, J. Zatarain Salazar, Diederik M. Roijers, F.A. Oliehoek, P.K. Murukannaiah
DOI related publication
https://doi.org/10.24963/ijcai.2023/755
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Z. MS Osika, J. Zatarain Salazar, Diederik M. Roijers, F.A. Oliehoek, P.K. Murukannaiah
Research Group
Policy Analysis
Pages (from-to)
6741-6749
ISBN (electronic)
9781956792034
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse problems, approaches for analyzing the trade-offs offered by MOO algorithms are scattered across fields. We provide an overview of the advances on this topic, including methods for visualization, mining the solution set, and uncertainty exploration as well as emerging research directions, including interactivity, explainability, and ethics. We synthesize these methods drawing from different fields of research to build a unified approach, independent of the application. Our goals are to reduce the entry barrier for researchers and practitioners on using MOO algorithms and to provide novel research directions.

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