A Collaborative Neurodynamic Algorithm for Quadratic Unconstrained Binary Optimization

Hongzong Li; Jun Wang

doi:10.1109/TETCI.2024.3405370

A Collaborative Neurodynamic Algorithm for Quadratic Unconstrained Binary Optimization

Hongzong Li, Jun Wang^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

2 Citations (Scopus)

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.

© 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

Original language	English
Pages (from-to)	228-239
Number of pages	12
Journal	IEEE Transactions on Emerging Topics in Computational Intelligence
Volume	9
Issue number	1
Online published	3 Jun 2024
DOIs	https://doi.org/10.1109/TETCI.2024.3405370
Publication status	Published - Feb 2025

Funding

This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China under Grant 11202318, Grant 11202019, and Grant 11203721, in part by InnoHK initiative, the Government of the Hong Kong Special Administrative Region, and in part by the Laboratory for AI-Powered Financial Technologies.

Research Keywords

Approximation algorithms
Boltzmann machine
Collaboration
collaborative neurodynamic optimization
combinatorial optimization
Convergence
discrete Hopfield network
Linear programming
Neurodynamics
Neurons
Optimization
Quadratic unconstrained binary optimization (QUBO)

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10.1109/TETCI.2024.3405370

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title = "A Collaborative Neurodynamic Algorithm for Quadratic Unconstrained Binary Optimization",

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.{\textcopyright} 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.",

keywords = "Approximation algorithms, Boltzmann machine, Collaboration, collaborative neurodynamic optimization, combinatorial optimization, Convergence, discrete Hopfield network, Linear programming, Neurodynamics, Neurons, Optimization, Quadratic unconstrained binary optimization (QUBO)",

author = "Hongzong Li and Jun Wang",

year = "2025",

month = feb,

doi = "10.1109/TETCI.2024.3405370",

language = "English",

volume = "9",

pages = "228--239",

number = "1",

}

TY - JOUR

T1 - A Collaborative Neurodynamic Algorithm for Quadratic Unconstrained Binary Optimization

AU - Li, Hongzong

AU - Wang, Jun

PY - 2025/2

Y1 - 2025/2

N2 - 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.© 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

AB - 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.© 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

KW - Approximation algorithms

KW - Boltzmann machine

KW - Collaboration

KW - collaborative neurodynamic optimization

KW - combinatorial optimization

KW - Convergence

KW - discrete Hopfield network

KW - Linear programming

KW - Neurodynamics

KW - Neurons

KW - Optimization

KW - Quadratic unconstrained binary optimization (QUBO)

UR - http://www.scopus.com/inward/record.url?scp=85195419714&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85195419714&origin=recordpage

U2 - 10.1109/TETCI.2024.3405370

DO - 10.1109/TETCI.2024.3405370

M3 - RGC 21 - Publication in refereed journal

AN - SCOPUS:85195419714

SN - 2471-285X

VL - 9

SP - 228

EP - 239

JO - IEEE Transactions on Emerging Topics in Computational Intelligence

JF - IEEE Transactions on Emerging Topics in Computational Intelligence

IS - 1

ER -

A Collaborative Neurodynamic Algorithm for Quadratic Unconstrained Binary Optimization

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GRF: Neurodynamics-driven Optimization and Control of Intelligent Heating, Ventilation and Air Conditioning Systems

GRF: Collaborative Neurodynamic Approaches to Portfolio Optimization

GRF: Intelligent Mission Planning and Tracking Control of Autonomous Surface Vehicles Based on Neural Computation

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A Collaborative Neurodynamic Algorithm for Quadratic Unconstrained Binary Optimization

Abstract

Funding

Research Keywords

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Other Access Options

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GRF: Neurodynamics-driven Optimization and Control of Intelligent Heating, Ventilation and Air Conditioning Systems

GRF: Collaborative Neurodynamic Approaches to Portfolio Optimization

GRF: Intelligent Mission Planning and Tracking Control of Autonomous Surface Vehicles Based on Neural Computation

Cite this