Hash Bit Selection Based on Collaborative Neurodynamic Optimization

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. Levy 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 Levy 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.

Automated summary

This article presents a method for solving the hash bit selection problem using a collaborative neurodynamic optimization approach, which uses Levy mutation to ensure convergence to global optima. Experimental results demonstrate the efficacy and superiority of this method on three benchmarks.