A collective neurodynamic approach to distributed constrained optimization

Qingshan Liu, Shaof Yang, Jun Wang

Research output: Contribution to journalArticlepeer-review

135 Citations (Scopus)

Abstract

This paper presents a collective neurodynamic approach with multiple interconnected recurrent neural networks (RNNs) for distributed constrained optimization. The objective function of the distributed optimization problems to be solved is a sum of local convex objective functions, which may be nonsmooth. Subject to its local constraints, each local objective function is minimized individually by using an RNN, with consensus among others. In contrast to existing continuous-time distributed optimization methods, the proposed collective neurodynamic approach is capable of solving more general distributed optimization problems. Simulation results on three numerical examples are discussed to substantiate the effectiveness and characteristics of the proposed approach. In addition, an application to the optimal placement problem is delineated to demonstrate the viability of the approach.

Original languageEnglish
Article number7452624
Pages (from-to)1747-1758
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume28
Issue number8
DOIs
Publication statusPublished - Aug 2017
Externally publishedYes

Keywords

  • Collective neurodynamics
  • consensus
  • distributed optimization
  • recurrent neural networks (RNNs)

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