A collaborative neurodynamic approach with two-timescale projection neural networks designed via majorization-minimization for global optimization and distributed global optimization

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number106525
Journal / PublicationNeural Networks
Volume179
Online published11 Jul 2024
Publication statusPublished - Nov 2024

Abstract

In this paper, two two-timescale projection neural networks are proposed based on the majorization-minimization principle for nonconvex optimization and distributed nonconvex optimization. They are proved to be globally convergent to Karush–Kuhn–Tucker points. A collaborative neurodynamic approach leverages multiple two-timescale projection neural networks repeatedly re-initialized using a meta-heuristic rule for global optimization and distributed global optimization. Two numerical examples are elaborated to demonstrate the efficacy of the proposed approaches. © 2024 Elsevier Ltd.

Research Area(s)

  • Collaborative neurodynamic optimization, Distributed optimization, Global optimization, Majorization-minimization principle, Projection neural network

Citation Format(s)

A collaborative neurodynamic approach with two-timescale projection neural networks designed via majorization-minimization for global optimization and distributed global optimization. / Li, Yangxia; Xia, Zicong; Liu, Yang et al.
In: Neural Networks, Vol. 179, 106525, 11.2024.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review