A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization

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Original languageEnglish
Article number9023556
Pages (from-to)36-48
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Issue number1
Online published3 Mar 2020
Publication statusPublished - Jan 2021


This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. The proposed approach employs two recurrent neural networks operating concurrently at two timescales. In addition, particle swarm optimization is used to update the initial neuronal states iteratively to escape from local minima toward better initial states. In spite of its minimal system complexity, the approach is proven to be almost surely convergent to optimal solutions. Its superior performance is substantiated via solving five benchmark problems.

Research Area(s)

  • Almost-sure convergence, mixed-integer optimization, neural networks