Average Quasi-Consensus Algorithm for Distributed Constrained Optimization: Impulsive Communication Framework
文献类型：期刊论文
| 作者 | He, Xing1,2 ; Yu, Junzhi1; Huang, Tingwen3; Li, Chuandong2; Li, Chaojie4
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| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
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| 出版日期 | 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. |
| WOS关键词 | NEURODYNAMIC APPROACH ; CONVEX-OPTIMIZATION ; GRADIENT ALGORITHM ; ECONOMIC-DISPATCH ; MULTIAGENT SYSTEM ; NETWORKS ; INITIALIZATION ; STABILITY |
| 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研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000511934000029 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 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) |
| 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.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Southwest Univ, Coll Elect & Informat Engn, Natl & Local Joint Engn Lab Intelligent Transmiss, Chongqing 400715, Peoples R China 3.Texas A&M Univ Qatar, Dept Math, Doha, Qatar 4.RMIT Univ, Sch Elect & Comp Engn, Melbourne, Vic 3001, Australia |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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