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

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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.
Original languageEnglish
Title of host publicationProceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
EditorsEdith Elkind
PublisherInternational Joint Conferences on Artificial Intelligence (IJCAI)
Pages6741-6749
Number of pages9
ISBN (Electronic)9781956792034
DOIs
Publication statusPublished - 2023
Event32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 - Macao, China
Duration: 19 Aug 202325 Aug 2023

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2023-August
ISSN (Print)1045-0823

Conference

Conference32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
Country/TerritoryChina
CityMacao
Period19/08/2325/08/23

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