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...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 32; číslo 1; s. 36 - 48
Hlavní autoři: Che, Hangjun, Wang, Jun
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2020.2973760