Distributed hybrid optimization for multi-agent systems

被引：10

作者：

Tan XueGang ^{[1
,2
]}

Yuan Yang ^{[2
]}

He WangLi ^{[2
]}

Cao JinDe ^{[1
]}

Huang TingWen ^{[3
]}

机构：

[1] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China

[2] East China Univ Sci & Technol, Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China

[3] Texas A&M Univ Qatar, Dept Sci, Doha 23874, Qatar

来源：

SCIENCE CHINA-TECHNOLOGICAL SCIENCES | 2022年 / 65卷 / 08期

基金：

中国国家自然科学基金;

关键词：

multi-agent systems; hybrid impulsive strategy; optimal consensus; distributed optimization; ALGORITHM; CONVERGENCE; CONSENSUS;

D O I：

10.1007/s11431-022-2060-7

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper addresses the distributed optimization problems of multi-agent systems using a distributed hybrid impulsive protocol. The objective is to ensure the agents achieve the state consensus and optimize the aggregate objective functions assigned for each agent with distributed manner. We establish two criteria related to the optimality condition and the impulsive gain upper estimation, and propose a distributed hybrid impulsive optimal protocol, which includes two terms: the local averaging term in the continuous interval and the term involving the gradient information at impulsive instants. The simulation results show that the optimal consensus can be realized under the distributed hybrid impulsive optimization algorithm.

引用

页码：1651 / 1660

页数：10

共 39 条

[1] Distributed Linearized Alternating Direction Method of Multipliers for Composite Convex Consensus Optimization [J].