Average Quasi-Consensus Algorithm for Distributed Constrained Optimization: Impulsive Communication Framework

doi:10.1109/TCYB.2018.2869249

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室

	Average Quasi-Consensus Algorithm for Distributed Constrained Optimization: Impulsive Communication Framework
	He, Xing 3,4; Yu, Junzhi 4; Huang, Tingwen 1; Li, Chuandong 3; Li, Chaojie 2
刊名	IEEE TRANSACTIONS ON CYBERNETICS
	2020
卷号	50 期号:1 页码:351-360
关键词	Distributed optimization impulsive average quasi-consensus algorithm impulsive communication framework multiagent networks
ISSN号	2168-2267
DOI	10.1109/TCYB.2018.2869249
通讯作者	Huang, Tingwen(tingwen.huang@qatar.tamu.edu)
英文摘要	This paper presents the impulsive average quasi-consensus algorithm for distributed constrained convex optimization. First, the constrained optimization problem can be transformed into an unconstrained problem using the interior point method, and then a distributed algorithm is modeled by means of impulsive differential equation. In the framework of the continuous-time gradient method and algebraic graph theory, each agent can deal with one local objective function with local constraints. At the impulsive instants, each agent can communicate with its neighboring agents over the network. Under certain conditions, the impulsive average quasi-consensus is achieved. It is shown that the state of average quasi-consensus is the optimal solution of the aforementioned unconstrained optimization problem, and the state of each agent can also reach the neighborhood of the optimal solution. Finally, two numerical examples show the effectiveness of the proposed impulsive average quasi-consensus algorithm. Moreover, the feasibility of the approach is verified by an application to one sensor network localization problem.
资助项目	Natural Science Foundation of China[61773320] ; Natural Science Foundation of China[61633011] ; Natural Science Foundation of China[61725305] ; Natural Science Foundation of China[61633020] ; China Post-Doctoral Science Foundation[2016M600144] ; China Post-Doctoral Science Foundation[2018T110154] ; Natural Science Foundation Project of Chongqing CSTC[cstc2018jcyjAX0583] ; Research Foundation of Key Laboratory of Machine Perception and Children's Intelligence Development - Chongqing University of Education, China[16xjpt07] ; NPRP from the Qatar National Research Fund (Qatar Foundation)[9-166-1-031]
WOS关键词	NEURODYNAMIC APPROACH ; CONVEX-OPTIMIZATION ; GRADIENT ALGORITHM ; ECONOMIC-DISPATCH ; MULTIAGENT SYSTEM ; NETWORKS ; INITIALIZATION ; STABILITY
WOS研究方向	Automation & Control Systems ; Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000511934000029
资助机构	Natural Science Foundation of China ; China Post-Doctoral Science Foundation ; Natural Science Foundation Project of Chongqing CSTC ; Research Foundation of Key Laboratory of Machine Perception and Children's Intelligence Development - Chongqing University of Education, China ; NPRP from the Qatar National Research Fund (Qatar Foundation)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/28558]
专题	自动化研究所_复杂系统管理与控制国家重点实验室
通讯作者	Huang, Tingwen
作者单位	1.Texas A&M Univ Qatar, Dept Math, Doha, Qatar 2.RMIT Univ, Sch Elect & Comp Engn, Melbourne, Vic 3001, Australia 3.Southwest Univ, Coll Elect & Informat Engn, Natl & Local Joint Engn Lab Intelligent Transmiss, Chongqing 400715, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	He, Xing,Yu, Junzhi,Huang, Tingwen,et al. Average Quasi-Consensus Algorithm for Distributed Constrained Optimization: Impulsive Communication Framework[J]. IEEE TRANSACTIONS ON CYBERNETICS,2020,50(1):351-360.
APA	He, Xing,Yu, Junzhi,Huang, Tingwen,Li, Chuandong,&Li, Chaojie.(2020).Average Quasi-Consensus Algorithm for Distributed Constrained Optimization: Impulsive Communication Framework.IEEE TRANSACTIONS ON CYBERNETICS,50(1),351-360.
MLA	He, Xing,et al."Average Quasi-Consensus Algorithm for Distributed Constrained Optimization: Impulsive Communication Framework".IEEE TRANSACTIONS ON CYBERNETICS 50.1(2020):351-360.