Multivehicle Task Assignment Based on Collaborative Neurodynamic Optimization With Discrete Hopfield Networks

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)5274-5286
Number of pages13
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Volume32
Issue number12
Online published2 Jun 2021
Publication statusPublished - Dec 2021

Abstract

This article presents a collaborative neurodynamic optimization (CNO) approach to multivehicle task assignments (TAs). The original combinatorial quadratic optimization problem for TA is reformulated as a quadratic unconstrained binary optimization (QUBO) problem with a quadratic utility function and a penalty function for handling load capacity and cooperation constraints. In the framework of CNO with a population of discrete Hopfield networks (DHNs), a TA algorithm is proposed for solving the formulated QUBO problem. Superior experimental results in four typical multivehicle operation scenarios are reported to substantiate the efficacy of the proposed neurodynamics-based TA approach.

Research Area(s)

  • Biological neural networks, Collaboration, Collaborative neurodynamic optimization (CNO), discrete Hopfield network (DHN), Indexes, Neurodynamics, Neurons, Optimization, Task analysis, task assignment (TA)

Citation Format(s)