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

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 1; pp. 36 - 48
• Main Authors: Che, Hangjun, Wang, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Almost-sure convergence; Benchmarking; Biological neural networks; Collaboration; Computer Simulation; Eigenvalues and eigenfunctions; Linear Models; Linear programming; Mixed integer; mixed-integer optimization; Neural networks; Neural Networks, Computer; Neurodynamics; Optimization; Particle swarm optimization; Problem Solving; Recurrent neural networks; Time
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2020.2973760

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.