南方科技大学知识苑(SUSTech KC): A Review on Evolutionary Multitask Optimization: Trends and Challenges

题名	A Review on Evolutionary Multitask Optimization: Trends and Challenges
作者	Wei, Tingyang 1; Wang, Shibin 2; Zhong, Jinghui 1,3; Liu, Dong 2; Zhang, Jun 4,5
通讯作者	Zhong, Jinghui
发表日期	2022-10-01
DOI	10.1109/TEVC.2021.3139437
发表期刊	IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 影响因子和分区
ISSN	1089-778X
EISSN	1941-0026
卷号	26 期号:5页码:941-960
摘要	Evolutionary algorithms (EAs) possess strong problem-solving abilities and have been applied in a wide range of applications. However, they still suffer from a high computational burden and poor generalization ability. To overcome the limitations, numerous studies consider conducting knowledge extraction across distinct optimization task domains. Among these research strands, one representative tributary is evolutionary multitask optimization (EMTO) that aims to resolve multiple optimization tasks simultaneously. The underlying attribute of implicit parallelism for EAs can well incorporate with the framework of EMTO, giving rise to the ascending EMTO studies. This review is intended to present a detailed exposition on the research in the EMTO area. We reveal the core components for designing the EMTO algorithms. Subsequently, we organize the works lying in the fusions between EMTO and traditional EAs. By analyzing the associations for diverse strategies in different branches of EMTO, this review uncovers the research trends and the potentially important directions, with additional interesting real-world applications mentioned.
关键词	Task analysis Optimization Statistics Sociology Multitasking Costs Evolutionary computation Evolutionary algorithm (EA) evolutionary multitasking transfer optimization
相关链接	[来源记录]
收录类别	SCI
语种	英语
学校署名	通讯
资助项目	Key Project of Science and Technology Innovation 2030 through the Ministry of Science and Technology of China[2018AAA0101304] ; National Natural Science Foundation of China["62072160","62076098"] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Guangdong Natural Science Foundation Research Team[2018B030312003]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:000862385200014
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
ESI学科分类	COMPUTER SCIENCE
来源库	Web of Science
全文链接	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9665768
引用统计	被引频次[WOS]：64
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/405986
专题	南方科技大学
作者单位	1.South China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Peoples R China 2.Henan Normal Univ, Coll Comp & Informat Engn, Xinxiang 453007, Henan, Peoples R China 3.Southern Univ Sci & Technol, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen 518055, Peoples R China 4.Hanyang Univ, Ansan 15588, South Korea 5.Chaoyang Univ Technol, Taichung 41349, Taiwan
通讯作者单位	南方科技大学
推荐引用方式 GB/T 7714	Wei, Tingyang,Wang, Shibin,Zhong, Jinghui,et al. A Review on Evolutionary Multitask Optimization: Trends and Challenges[J]. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,2022,26(5):941-960.
APA	Wei, Tingyang,Wang, Shibin,Zhong, Jinghui,Liu, Dong,&Zhang, Jun.(2022).A Review on Evolutionary Multitask Optimization: Trends and Challenges.IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION,26(5),941-960.
MLA	Wei, Tingyang,et al."A Review on Evolutionary Multitask Optimization: Trends and Challenges".IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 26.5(2022):941-960.