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

Hangjun Che, Jun Wang

2020IEEE Transactions on Neural Networks and Learning Systems108 citationsDOI

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.

Topics & Concepts

Maxima and minimaBenchmark (surveying)Mathematical optimizationInteger (computer science)Constrained optimization problemOptimization problemParticle swarm optimizationDuplex (building)Computer scienceMathematicsGeodesyGeographyProgramming languageDNAMathematical analysisBiologyGeneticsNeural Networks and ApplicationsMachine Learning and ELMAdvanced Memory and Neural Computing