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

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.

Automated summary

This article introduces a two-timescale duplex neurodynamic approach to mixed-integer optimization, utilizing two recurrent neural networks operating concurrently at two different timescales and employing particle swarm optimization for iterative updating of initial neuronal states. Despite its minimal system complexity, the method is almost surely convergent to optimal solutions and its superior performance is demonstrated through solving benchmark problems.