This study proposes the use of a novel integrated framework for 2D en route airspace sub-sectorization.The integrated framework combines the multi-commodity flow optimization approach,complex network cluster-ing appro...This study proposes the use of a novel integrated framework for 2D en route airspace sub-sectorization.The integrated framework combines the multi-commodity flow optimization approach,complex network cluster-ing approach,and Minimum Bounding Geometry(MBG)-coupled Rule-based Approach for boundary design.A decomposition-based discrete particle swarm optimization(DPSO)is used to solve the clustering problem.The output of the flow optimization is used as a guiding standard for the DPSO.Experimentations were performed using the Indian airspace sector to validate the framework and DPSO was run for different maximum number of generations(maxgen).The findings reveal that the multi-commodity flow approach captures system-wide flow operations.Clustering results corresponding to maxgen=100 and maxgen=150 perform best in terms of equitable and balanced distribution of cluster size and traffic load.The MBG-coupled Rule-based Approach leads to com-pact and convex sub-sector boundary design.Major implications of this research include dynamic adaptability of the integrated framework,increased sensitivity of sector design to network evolution,and a computationally tractable framework.The higher controllability of the proposed framework also offers an increased acceptance among practitioners.展开更多
The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale netwo...The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale networks. To identify the community structure of a large-scale network with high speed and high quality, in this paper, we propose a fast community detection algorithm, the F-Attractor, which is based on the distance dynamics model. The main contributions of the F-Attractor are as follows. First, we propose the use of two prejudgment rules from two different perspectives： node and edge. Based on these two rules, we develop a strategy of internal edge prejudgment for predicting the internal edges of the network. Internal edge prejudgment can reduce the number of edges and their neighbors that participate in the distance dynamics model. Second, we introduce a triangle distance to further enhance the speed of the interaction process in the distance dynamics model. This triangle distance uses two known distances to measure a third distance without any extra computation. We combine the above techniques to improve the distance dynamics model and then describe the community detection process of the F-Attractor. The results of an extensive series of experiments demonstrate that the F-Attractor offers high-speed community detection and high partition quality.展开更多
基金PMRF PM/MHRD-20-16823.03 for the financial support。
文摘This study proposes the use of a novel integrated framework for 2D en route airspace sub-sectorization.The integrated framework combines the multi-commodity flow optimization approach,complex network cluster-ing approach,and Minimum Bounding Geometry(MBG)-coupled Rule-based Approach for boundary design.A decomposition-based discrete particle swarm optimization(DPSO)is used to solve the clustering problem.The output of the flow optimization is used as a guiding standard for the DPSO.Experimentations were performed using the Indian airspace sector to validate the framework and DPSO was run for different maximum number of generations(maxgen).The findings reveal that the multi-commodity flow approach captures system-wide flow operations.Clustering results corresponding to maxgen=100 and maxgen=150 perform best in terms of equitable and balanced distribution of cluster size and traffic load.The MBG-coupled Rule-based Approach leads to com-pact and convex sub-sector boundary design.Major implications of this research include dynamic adaptability of the integrated framework,increased sensitivity of sector design to network evolution,and a computationally tractable framework.The higher controllability of the proposed framework also offers an increased acceptance among practitioners.
基金supported by the National Natural Science Foundation of China(Nos.61573299,61174140,61472127,and 61272395)the Social Science Foundation of Hunan Province(No.16ZDA07)+2 种基金China Postdoctoral Science Foundation(Nos.2013M540628and 2014T70767)the Natural Science Foundation of Hunan Province(Nos.14JJ3107 and 2017JJ5064)the Excellent Youth Scholars Project of Hunan Province(No.15B087)
文摘The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale networks. To identify the community structure of a large-scale network with high speed and high quality, in this paper, we propose a fast community detection algorithm, the F-Attractor, which is based on the distance dynamics model. The main contributions of the F-Attractor are as follows. First, we propose the use of two prejudgment rules from two different perspectives： node and edge. Based on these two rules, we develop a strategy of internal edge prejudgment for predicting the internal edges of the network. Internal edge prejudgment can reduce the number of edges and their neighbors that participate in the distance dynamics model. Second, we introduce a triangle distance to further enhance the speed of the interaction process in the distance dynamics model. This triangle distance uses two known distances to measure a third distance without any extra computation. We combine the above techniques to improve the distance dynamics model and then describe the community detection process of the F-Attractor. The results of an extensive series of experiments demonstrate that the F-Attractor offers high-speed community detection and high partition quality.
