Hash Bit Selection Based on Collaborative Neurodynamic Optimization

Xinqi LI, Jun WANG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

7 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)11144-11155
Number of pages12
JournalIEEE Transactions on Cybernetics
Volume52
Issue number10
Early online date20 Aug 2021
DOIs
Publication statusPublished - Oct 2022
Externally publishedYes

Bibliographical note

This work was supported in part by the Key Project of Science and Technology Innovation 2030 through the Ministry of Science and Technology of China under Grant 2018AAA0100300 and Grant 2018AAA0101301; and in part by the Research Grants Council of the Hong Kong Special Administrative Region of China through General Research Fund under Grant 11208517, Grant 11202318, Grant 11202019, Grant 11209819, and Grant 11203820.

Keywords

  • Global optimization
  • hash bit selection
  • neurodynamic optimization

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