CSpace  > 系统科学研究所
Interactive Multiobjective Optimization: A Review of the State-of-the-Art
Xin, Bin1,2,3; Chen, Lu1,2; Chen, Jie1,2,3; Ishibuchi, Hisao4; Hirota, Kaoru1; Liu, Bo5
2018
发表期刊IEEE ACCESS
ISSN2169-3536
卷号6页码:41256-41279
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.
关键词Evolutionary multiobjective optimization interactive multiobjective optimization multiple criteria decision making preference information preference models
DOI10.1109/ACCESS.2018.2856832
语种英语
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
WOS类目Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号WOS:000441868800082
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/31180
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.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Xin, Bin]的文章
[Chen, Lu]的文章
[Chen, Jie]的文章
百度学术
百度学术中相似的文章
[Xin, Bin]的文章
[Chen, Lu]的文章
[Chen, Jie]的文章
必应学术
必应学术中相似的文章
[Xin, Bin]的文章
[Chen, Lu]的文章
[Chen, Jie]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。