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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| Published in: | IEEE transaction on neural networks and learning systems Vol. 32; no. 12; pp. 5274 - 5286 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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01.12.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2162-237X, 2162-2388, 2162-2388 |
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| 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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| AbstractList | 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. 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.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. |
| Author | Wang, Jiasen Wang, Jun Han, Qing-Long |
| Author_xml | – sequence: 1 givenname: Jiasen orcidid: 0000-0001-5687-5947 surname: Wang fullname: Wang, Jiasen email: jiasenwangcs@yeah.net organization: Future Network Research Center, Purple Mountain Laboratories, Nanjing, China – sequence: 2 givenname: Jun orcidid: 0000-0002-1305-5735 surname: Wang fullname: Wang, Jun email: jwang.cs@cityu.edu.hk organization: Department of Computer Science and the School of Data Science, City University of Hong Kong, Hong Kong – sequence: 3 givenname: Qing-Long orcidid: 0000-0002-7207-0716 surname: Han fullname: Han, Qing-Long email: qhan@swin.edu.au organization: School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, VIC, Australia |
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| SubjectTerms | 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) |
| Title | Multivehicle Task Assignment Based on Collaborative Neurodynamic Optimization With Discrete Hopfield Networks |
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