Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
A Two-Timescale Duplex Neurodynamic Approach to Mixed-Integer Optimization. |
| Συγγραφείς: |
Che, Hangjun, Wang, Jun |
| Πηγή: |
IEEE Transactions on Neural Networks & Learning Systems; Jan2021, Vol. 32 Issue 1, p36-48, 13p |
| Θεματικοί όροι: |
Recurrent neural networks, Particle swarm optimization, Benchmark problems (Computer science), Biological neural networks, Mixed integer linear programming |
| Περίληψη: |
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. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Biomedical Index |