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Hash Bit Selection Based on Collaborative Neurodynamic Optimization
- Source :
- IEEE Transactions on Cybernetics. 52:11144-11155
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
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.
- Subjects :
- Mathematical optimization
Computer science
Hash function
Particle swarm optimization
Binary number
Models, Theoretical
Computer Science Applications
Human-Computer Interaction
Cardinality
Control and Systems Engineering
Mutation (genetic algorithm)
Astrophysics::Solar and Stellar Astrophysics
Quadratic programming
Electrical and Electronic Engineering
Algorithms
Software
Selection (genetic algorithm)
Information Systems
Premature convergence
Subjects
Details
- ISSN :
- 21682275 and 21682267
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Cybernetics
- Accession number :
- edsair.doi.dedup.....09f2067da3efcece286a8a282eac722b