文献类型：期刊文献

英文题名：Towards fairness-aware multi-objective optimization

作者：Yu, Guo[1];Ma, Lianbo[2];Wang, Xilu[3];Du, Wei[4];Du, Wenli[4];Jin, Yaochu[5]

机构：[1]Nanjing Tech Univ, Inst Intelligent Mfg, Nanjing 211816, Peoples R China;[2]Northeastern Univ, Software Coll, Shenyang 110819, Peoples R China;[3]Bielefeld Univ, Fac Technol, D-33619 Bielefeld, Germany;[4]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[5]Westlake Univ, Sch Engn, Hangzhou 310030, Peoples R China

年份：2024

卷号：11

期号：1

外文期刊名：COMPLEX & INTELLIGENT SYSTEMS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001359456900001)】；

基金：This work was funded by National Natural Science Foundation of China (Nos. 62103150, 62333010, 62136003, 62173144), China Postdoctoral Science Foundation (No. 2021M691012), National Natural Science Foundation of Shanghai (21ZR1416100), Shanghai Rising-Star Program (22QA1402400).

语种：英文

外文关键词：Fairness-aware multi-objective optimization; Preference; Fairness-aware machine learning; Data-driven optimization; Federated optimization

摘要：Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications. However, much less attention has been paid to the fairness-aware multi-objective optimization, which is indeed commonly seen in real life, such as fair resource allocation problems and data-driven multi-objective optimization problems. This paper aims to illuminate and broaden our understanding of multi-objective optimization from the perspective of fairness. To this end, we start with a discussion of user preferences in multi-objective optimization. Subsequently, we explore its relationship to fairness in machine learning and multi-objective optimization. Following the above discussions, representative cases of fairness-aware multi-objective optimization are presented, further elaborating the importance of fairness in traditional multi-objective optimization, data-driven optimization and federated optimization. Finally, challenges and opportunities in fairness-aware multi-objective optimization are addressed. We hope that this article makes a solid step forward towards understanding fairness in the context of optimization. Additionally, we aim to promote research interests in fairness-aware multi-objective optimization.