Wang J, Wang J, Han QL. Multivehicle Task Assignment Based on Collaborative Neurodynamic Optimization With Discrete Hopfield Networks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021;
32:5274-5286. [PMID:
34077371 DOI:
10.1109/tnnls.2021.3082528]
[Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
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.
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