Multivehicle Task Assignment Based on Collaborative Neurodynamic Optimization With Discrete Hopfield Networks
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 5274-5286 |
Number of pages | 13 |
Journal / Publication | IEEE Transactions on Neural Networks and Learning Systems |
Volume | 32 |
Issue number | 12 |
Online published | 2 Jun 2021 |
Publication status | Published - Dec 2021 |
Link(s)
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)
Multivehicle Task Assignment Based on Collaborative Neurodynamic Optimization With Discrete Hopfield Networks. / Wang, Jiasen; Wang, Jun; Han, Qing-Long.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 12, 12.2021, p. 5274-5286.
In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 12, 12.2021, p. 5274-5286.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review