Analyses of inverted generational distance for many-objective optimisation algorithms

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Analyses of inverted generational distance for many-objective optimisation algorithms
	Cai, Xingjuan 1; Zhang, Maoqing 2; Wang, Hui 3; Xu, Meng 4; Chen, Jinjun 5; Zhang, Wensheng6
发表期刊	INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
ISSN	1758-0366
	2019
卷号	14 期号:1 页码:62-68
通讯作者	Zhang, Maoqing(maoqing_zhang@163.com)
摘要	Inverted generational distance is a widely used indicator for evaluating many-objective optimisation algorithms. In the past several years, numerous researchers have paid much attention to the improvement of many-objective optimisation algorithms, while few researchers have mathematically analysed inverted generational distance. In this paper, we present detailed mathematical analyses of inverted generational distance, and then reveal the relation between generational distance and inverted generational distance. The conclusion is drawn that convergence plays different roles in different stages. Experimental results on seven many-objective benchmark problems verify our analyses.
关键词	inverted generational distance IGD generational distance many-objective optimisation algorithm mathematical analyses
关键词[WOS]	PERFORMANCE
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61663028] ; National Natural Science Foundation of China[51775385] ; National Natural Science Foundation of China[61703279] ; Natural Science Foundation of Shanxi Province[201801D121127] ; Scientific and Technological innovation Team of Shanxi Province[201805D131007] ; PhD Research Startup Foundation of Taiyuan University of Science and Technology[20182002] ; National Natural Science Foundation of China[61806138] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61663028] ; National Natural Science Foundation of China[51775385] ; National Natural Science Foundation of China[61703279] ; Natural Science Foundation of Shanxi Province[201801D121127] ; Scientific and Technological innovation Team of Shanxi Province[201805D131007] ; PhD Research Startup Foundation of Taiyuan University of Science and Technology[20182002]
项目资助者	National Natural Science Foundation of China ; Natural Science Foundation of Shanxi Province ; Scientific and Technological innovation Team of Shanxi Province ; PhD Research Startup Foundation of Taiyuan University of Science and Technology
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:000482130100004
出版者	INDERSCIENCE ENTERPRISES LTD
引用统计	被引频次：21[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/27557
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Zhang, Maoqing
作者单位	1.Taiyuan Univ Sci & Technol, Complex Syst & Computat Intelligence Lab, Taiyuan 030024, Shanxi, Peoples R China 2.Tongji Univ, Dept Control Sci & Engn, Shanghai 201804, Peoples R China 3.Nanchang Inst Technol, Sch Informat Engn, Nanchang 330099, Jiangxi, Peoples R China 4.Beijing Univ Technol, Informat Fac, Beijing 100124, Peoples R China 5.Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne, Vic 3000, Australia 6.Chinese Acad Sci, State Key Lab Intelligent Control & Management Co, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Cai, Xingjuan,Zhang, Maoqing,Wang, Hui,et al. Analyses of inverted generational distance for many-objective optimisation algorithms[J]. INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION,2019,14(1):62-68.
APA	Cai, Xingjuan,Zhang, Maoqing,Wang, Hui,Xu, Meng,Chen, Jinjun,&Zhang, Wensheng.(2019).Analyses of inverted generational distance for many-objective optimisation algorithms.INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION,14(1),62-68.
MLA	Cai, Xingjuan,et al."Analyses of inverted generational distance for many-objective optimisation algorithms".INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 14.1(2019):62-68.