Privacy protection based on many-objective optimization algorithm

doi:10.1002/cpe.5342

	Privacy protection based on many-objective optimization algorithm
	Zhang, Jiangjiang 1; Xue, Fei 2; Cai, Xingjuan 1; Cui, Zhihua 1; Chang, Yu 1; Zhang, Wensheng 3; Li, Wuzhao 4
刊名	CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
	2019-10-25
卷号	31 期号:20 页码:14
关键词	achievement scalarizing function angle information index balanceable fitness estimation method many-objective optimization algorithm privacy protection
ISSN号	1532-0626
DOI	10.1002/cpe.5342
通讯作者	Cui, Zhihua(cuizhihua@gmail.com)
英文摘要	It is difficult to protect users' privacy and to process private information due to the complexity and uncertainty of such information. To protect private information quickly and accurately, a many-objective optimization algorithm framework based on the hybrid elite selection strategy is proposed in this paper. First, a mating selection mechanism combined with the achievement scale function and angle information index is used to generate elite offspring of the internal population. Then, the balanceable fitness estimation method is employed to select and update the external archive. To test performance, the proposed algorithm is tested on many-objective optimization problems (MaOPs) and compared with five state-of-the-art algorithms. Experimental simulation results show that the proposed algorithm is more effective in solving MaOPs and can inspire development of a better privacy protection strategy.
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] ; Natural Science Foundation of Shanxi Province[201801D121127] ; PhD Research Startup Foundation of Taiyuan University of Science and Technology[20182002] ; Zhejiang Provincial Natural Science Foundation of China[Y18F030036]
WOS关键词	PARTICLE SWARM OPTIMIZATION ; CUCKOO SEARCH ALGORITHM ; NSGA-III ALGORITHM ; BAT ALGORITHM ; EVOLUTIONARY ALGORITHM ; GENETIC ALGORITHM ; FIREFLY ALGORITHM ; PERFORMANCE ; POPULATIONS ; OPERATOR
WOS研究方向	Computer Science
语种	英语
出版者	WILEY
WOS记录号	WOS:000489324000007
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Shanxi Province ; PhD Research Startup Foundation of Taiyuan University of Science and Technology ; Zhejiang Provincial Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/26648]
专题	中国科学院自动化研究所
通讯作者	Cui, Zhihua
作者单位	1.Taiyuan Univ Sci & Technol, Complex Syst & Computat Intelligence Lab, Taiyuan 030024, Shanxi, Peoples R China 2.Beijing Wuzi Univ, Sch Informat, Beijing, Peoples R China 3.Chinese Acad Sci, State Key Lab Intelligent Control & Management Co, Inst Automat, Beijing, Peoples R China 4.Jiaxing Vocat Tech Coll, Jiaxing, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Jiangjiang,Xue, Fei,Cai, Xingjuan,et al. Privacy protection based on many-objective optimization algorithm[J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE,2019,31(20):14.
APA	Zhang, Jiangjiang.,Xue, Fei.,Cai, Xingjuan.,Cui, Zhihua.,Chang, Yu.,...&Li, Wuzhao.(2019).Privacy protection based on many-objective optimization algorithm.CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE,31(20),14.
MLA	Zhang, Jiangjiang,et al."Privacy protection based on many-objective optimization algorithm".CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 31.20(2019):14.