Distributed initialization-free algorithms for multi-agent optimization problems with coupled inequality constraints

Peng Li, Yiyi Zhao, Jiangping Hu, Yuping Zhang, Bijoy Kumar Ghosh

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This paper studies a resource optimization problem for a multi-agent network where all agents have local objective functions and local constraint sets. Meanwhile, the decision variables of all the agents need to satisfy a set of globally coupled inequality constraints. Then, a distributed continuous-time algorithm is designed with the help of the nonsmooth analysis theory and projection operator method. The proposed algorithm does not require each agent to send the gradient information of the cost function to its neighbors, which prevents the gradient information of agents leaking out. Moreover, the convergence analysis shows that the proposed algorithm can converge to the optimal solution starting from any initial allocation. Finally, a case study is presented for a resource allocation problem of a hybrid water-power network.

Original languageEnglish
Pages (from-to)155-162
Number of pages8
JournalNeurocomputing
Volume407
DOIs
StatePublished - Sep 24 2020

Keywords

  • Coupled inequality constraint
  • Distributed algorithm
  • Multi-agent optimization
  • Nonsmooth function
  • Projection operator

Fingerprint

Dive into the research topics of 'Distributed initialization-free algorithms for multi-agent optimization problems with coupled inequality constraints'. Together they form a unique fingerprint.

Cite this