Hash Bit Selection Based on Collaborative Neurodynamic Optimization
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 11144-11155 |
Number of pages | 12 |
Journal / Publication | IEEE Transactions on Cybernetics |
Volume | 52 |
Issue number | 10 |
Online published | 20 Aug 2021 |
Publication status | Published - Oct 2022 |
Link(s)
Abstract
Hash bit selection determines an optimal subset of hash bits from a candidate bit pool. It is formulated as a zero-one quadratic programming problem subject to binary and cardinality constraints. In this article, the problem is equivalently reformulated as a global optimization problem. A collaborative neurodynamic optimization (CNO) approach is applied to solve the problem by using a group of neurodynamic models initialized with particle swarm optimization iteratively in the CNO. Lévy mutation is used in the CNO to avoid premature convergence by ensuring initial state diversity. A theoretical proof is given to show that the CNO with the Lévy mutation operator is almost surely convergent to global optima. Experimental results are discussed to substantiate the efficacy and superiority of the CNO-based hash bit selection method to the existing methods on three benchmarks.
Research Area(s)
- Data models, Global optimization, hash bit selection, Lagrangian functions, Neural networks, neurodynamic optimization., Neurodynamics, Optimization, Particle swarm optimization, Symmetric matrices
Citation Format(s)
Hash Bit Selection Based on Collaborative Neurodynamic Optimization. / Li, Xinqi; Wang, Jun; Kwong, Sam.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 10, 10.2022, p. 11144-11155.
In: IEEE Transactions on Cybernetics, Vol. 52, No. 10, 10.2022, p. 11144-11155.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review