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

IEEE Transactions on Neural Networks and Learning Systems
Hangjun Che, Jun Wang

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.

Citations

Jan 27, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Yadi WangJun Wang
May 22, 2021·Neural Networks : the Official Journal of the International Neural Network Society·Yadi WangHangjun Che

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Software Mentioned

GA
MATLAB Global Optimization Toolbox
qquad
NOMAD
BONMIN
Omega
OPTI Toolbox

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