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

Hangjun Che, Jun Wang

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number9023556
Pages (from-to)36-48
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume32
Issue number1
DOIs
Publication statusPublished - Jan 2021
Externally publishedYes

Keywords

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

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