A Collaborative Neurodynamic Algorithm for Quadratic Unconstrained Binary Optimization
Hongzong Li, Jun Wang
Abstract
Quadratic unconstrained binary optimization (QUBO) is a typical combinatorial optimization problem with widespread applications in science, engineering, and business. As QUBO problems are usually NP-hard, conventional QUBO algorithms are very time-consuming for solving large-scale QUBO problems. In this paper, we present a collaborative neurodynamic optimization algorithm for QUBO. In the proposed algorithm, multiple discrete Hopfield networks, Boltzmann machines, or their variants are employed for scattered searches, and a particle swarm optimization rule is used to re-initialize neuronal states repeatedly toward global optima. With extensive experimental results on four classic combinatorial optimization problems, we demonstrate the efficacy and potency of the algorithm against several prevailing exact and meta-heuristic algorithms.