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 Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 106525 |
Journal / Publication | Neural Networks |
Volume | 179 |
Online published | 11 Jul 2024 |
Publication status | Published - Nov 2024 |
Link(s)
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.
In: Neural Networks, Vol. 179, 106525, 11.2024.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review