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

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 an...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 32; H. 12; S. 5274 - 5286
Hauptverfasser: Wang, Jiasen, Wang, Jun, Han, Qing-Long
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Biological neural networks
Collaboration
Collaborative neurodynamic optimization (CNO)
Combinatorial analysis
discrete Hopfield network (DHN)
Hopfield neural networks
Neurodynamics
Optimization
Penalty function
Task analysis
task assignment (TA)
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung: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.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3082528