Interactive Multiobjective Optimization: A Review of the State-of-the-Art

doi:10.1109/ACCESS.2018.2856832

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	Interactive Multiobjective Optimization: A Review of the State-of-the-Art
	Xin, Bin 1,2,3; Chen, Lu 1,2; Chen, Jie 1,2,3; Ishibuchi, Hisao 4; Hirota, Kaoru 1; Liu, Bo5
刊名	IEEE ACCESS
	2018
卷号	6 页码:41256-41279
关键词	Evolutionary multiobjective optimization interactive multiobjective optimization multiple criteria decision making preference information preference models
ISSN号	2169-3536
DOI	10.1109/ACCESS.2018.2856832
英文摘要	Interactive multiobjective optimization (IMO) aims at finding the most preferred solution of a decision maker with the guidance of his/her preferences which are provided progressively. During the process, the decision maker can adjust his/her preferences and explore only interested regions of the search space. In recent decades, IMO has gradually become a common interest of two distinct communities, namely, the multiple criteria decision making (MCDM) and the evolutionary multiobjective optimization (EMO). The IMO methods developed by the MCDM community usually use the mathematical programming methodology to search for a single preferred Pareto optimal solution, while those which are rooted in EMO often employ evolutionary algorithms to generate a representative set of solutions in the decision maker's preferred region. This paper aims to give a review of IMO research from both MCDM and EMO perspectives. Taking into account four classification criteria including the interaction pattern, preference information, preference model, and search engine (i.e., optimization algorithm), a taxonomy is established to identify important IMO factors and differentiate various IMO methods. According to the taxonomy, state-of-the-art IMO methods are categorized and reviewed and the design ideas behind them are summarized. A collection of important issues, e.g., the burdens, cognitive biases and preference inconsistency of decision makers, and the performance measures and metrics for evaluating IMO methods, are highlighted and discussed. Several promising directions worthy of future research are also presented.
资助项目	National Natural Science Foundation of China[61673058] ; National Natural Science Foundation of China[71101139] ; NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization[U1609214] ; Foundation for Innovative Research Groups of the National Natural Science Foundation of China[61621063] ; Projects of Major International (Regional) Joint Research Program NSFC[61720106011]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000441868800082
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/31179]
专题	系统科学研究所
通讯作者	Xin, Bin; Chen, Lu
作者单位	1.Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China 2.Beijing Inst Technol, State Key Lab Intelligent Control & Decis Complex, Beijing 100081, Peoples R China 3.Beijing Inst Technol, Beijing Adv Innovat Ctr Intelligent Robots & Syst, Beijing 100081, Peoples R China 4.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China 5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Xin, Bin,Chen, Lu,Chen, Jie,et al. Interactive Multiobjective Optimization: A Review of the State-of-the-Art[J]. IEEE ACCESS,2018,6:41256-41279.
APA	Xin, Bin,Chen, Lu,Chen, Jie,Ishibuchi, Hisao,Hirota, Kaoru,&Liu, Bo.(2018).Interactive Multiobjective Optimization: A Review of the State-of-the-Art.IEEE ACCESS,6,41256-41279.
MLA	Xin, Bin,et al."Interactive Multiobjective Optimization: A Review of the State-of-the-Art".IEEE ACCESS 6(2018):41256-41279.