The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a...The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.展开更多
With the widespread adoption of unmanned aerial vehicle(UAV)technology,task scheduling for UAV swarms has become a crucial approach to improve operational efficiency.Most existing studies oversimplify the operational ...With the widespread adoption of unmanned aerial vehicle(UAV)technology,task scheduling for UAV swarms has become a crucial approach to improve operational efficiency.Most existing studies oversimplify the operational process rules of UAVs,making it difficult to accurately characterize the adaptability differences of UAVs to various tasks under practical operational constraints.To address this limitation,this paper proposes a UAV swarm task scheduling problem with limited communication range(UAVS-LCR)and establishes an integer programming model for its formal description.For solving this problem,a multi-neighborhood iterative local search(MNILS)algorithm is designed,which adopts a doubly linked list solution representation method to reduce the computational complexity of basic neighborhood operations.This algorithm generates high-quality initial solutions via a greedy construction strategy,combines insertion search,multi-swap search and the two-opt operator to enable alternating exploration across multiple neighborhoods,and incorporates a simulated annealing mechanism to balance search efficiency and solution diversity.This method can provide an effective solution for various application scenarios including wide-area UAV inspection and heterogeneous UAV collaborative operations.Experimental results on 12 power grid maintenance test instances demonstrate that the MNILS algorithm significantly outperforms the genetic algorithm,the artificial bee colony algorithm,the ant colony optimization algorithm and the variable neighborhood search algorithm in terms of both solution quality and scalability for large-scale problems.展开更多
Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physi...Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physical attendance ofthe populations of students, professors, employees, and other members on campus. This research proposes an automated scheduling approach that can help universities and schools comply with the social distancing regulations by providingassistance in avoiding huge assemblages of people. Furthermore, this paper proposes a novel course timetable-scheduling scheme based on four main constraints.First, a distance of two meters must be maintained between each student inside theclassroom. Second, no classrooms should contain more than 20% of their regularcapacity. Third, there would be no back-to-back classes. Lastly, no lectures shouldbe held simultaneously in adjacent classrooms. The proposed approach wasimplemented using a variable neighborhood search (VNS) approach with an adaptive neighborhood structure (AD-NS) to resolve the problem of scheduling coursetimetables at Al-Ahlyyia Amman University. However, the experimental resultsshow that the proposed techniques outperformed the standard VNS tested on university course timetabling benchmark dataset ITC2007-Track3. Meanwhile, theapproach was tested using datasets collected from the faculty of information technology at Al-Ahlyyia Amman University (Jordan). Where the results showed that,the proposed technique could help educational institutes to resume their regularoperations while complying with the social distancing guidelines.展开更多
文摘The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.
基金supported by the Project Social Science Foundation Jiangsu Province(No.22GLB026)2025 National Major Project for Logistics Education Reform and Research in Higher Education and Vocational Colleges(No.JZW2025002)。
文摘With the widespread adoption of unmanned aerial vehicle(UAV)technology,task scheduling for UAV swarms has become a crucial approach to improve operational efficiency.Most existing studies oversimplify the operational process rules of UAVs,making it difficult to accurately characterize the adaptability differences of UAVs to various tasks under practical operational constraints.To address this limitation,this paper proposes a UAV swarm task scheduling problem with limited communication range(UAVS-LCR)and establishes an integer programming model for its formal description.For solving this problem,a multi-neighborhood iterative local search(MNILS)algorithm is designed,which adopts a doubly linked list solution representation method to reduce the computational complexity of basic neighborhood operations.This algorithm generates high-quality initial solutions via a greedy construction strategy,combines insertion search,multi-swap search and the two-opt operator to enable alternating exploration across multiple neighborhoods,and incorporates a simulated annealing mechanism to balance search efficiency and solution diversity.This method can provide an effective solution for various application scenarios including wide-area UAV inspection and heterogeneous UAV collaborative operations.Experimental results on 12 power grid maintenance test instances demonstrate that the MNILS algorithm significantly outperforms the genetic algorithm,the artificial bee colony algorithm,the ant colony optimization algorithm and the variable neighborhood search algorithm in terms of both solution quality and scalability for large-scale problems.
文摘Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physical attendance ofthe populations of students, professors, employees, and other members on campus. This research proposes an automated scheduling approach that can help universities and schools comply with the social distancing regulations by providingassistance in avoiding huge assemblages of people. Furthermore, this paper proposes a novel course timetable-scheduling scheme based on four main constraints.First, a distance of two meters must be maintained between each student inside theclassroom. Second, no classrooms should contain more than 20% of their regularcapacity. Third, there would be no back-to-back classes. Lastly, no lectures shouldbe held simultaneously in adjacent classrooms. The proposed approach wasimplemented using a variable neighborhood search (VNS) approach with an adaptive neighborhood structure (AD-NS) to resolve the problem of scheduling coursetimetables at Al-Ahlyyia Amman University. However, the experimental resultsshow that the proposed techniques outperformed the standard VNS tested on university course timetabling benchmark dataset ITC2007-Track3. Meanwhile, theapproach was tested using datasets collected from the faculty of information technology at Al-Ahlyyia Amman University (Jordan). Where the results showed that,the proposed technique could help educational institutes to resume their regularoperations while complying with the social distancing guidelines.
