Please use this identifier to cite or link to this item:
Title: Distributed optimization for two types of heterogeneous multiagent systems
Authors: Sun, Chao
Ye, Maojiao
Hu, Guoqiang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Sun, C., Ye, M. & Hu, G. (2020). Distributed optimization for two types of heterogeneous multiagent systems. IEEE Transactions On Neural Networks and Learning Systems, 32(3), 1314-1324.
Project: RG180/17(2017-T1- 002-158)
Journal: IEEE Transactions on Neural Networks and Learning Systems 
Abstract: This article studies distributed optimization algorithms for heterogeneous multiagent systems under an undirected and connected communication graph. Two types of heterogeneities are discussed. First, we consider a class of multiagent systems composed of both continuous-time dynamic agents and discrete-time dynamic agents. The agents coordinate with each other to minimize a global objective function that is the sum of their local convex objective functions. A distributed subgradient method is proposed for each agent in the network. It is proved that driven by the proposed updating law, the agents' position states converge to an optimal solution of the optimization problem, provided that the subgradients of the objective functions are bounded, the step size is not summable but square summable, and the sampling period is bounded by some constant. Second, we consider a class of multiagent systems composed of both first-order dynamic agents and second-order dynamic agents. It is proved that the agents' position states converge to the unique optimal solution if the objective functions are strongly convex, continuously differentiable, and the gradients are globally Lipschitz. Numerical examples are given to verify the conclusions.
ISSN: 2162-2388
DOI: 10.1109/TNNLS.2020.2984584
Schools: School of Electrical and Electronic Engineering 
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Citations 20

Updated on Nov 26, 2023

Web of ScienceTM
Citations 20

Updated on Oct 25, 2023

Page view(s)

Updated on Nov 30, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.