The establishment of district metered areas (DMAs) is a highly effective method to mitigate operational management difficulties and enhance the efficiency of water distribution networks (WDNs). There are several objec...
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The establishment of district metered areas (DMAs) is a highly effective method to mitigate operational management difficulties and enhance the efficiency of water distribution networks (WDNs). There are several objectives associated with the DMA's design that are contingent upon the network parameters that are affected by its formation. Two previous studies considered DMA design as a three-objective problem (operational cost, average pressure and water age) and a four-objective problem (design cost, pressure deviation, resilience index (RI) and demand shortfall) for pumped and gravity networks, respectively. The problems were addressed through the implementation of a multi-phase DMA design methodology using the NSGA-II and NSGA-III optimisation tools, respectively. The present work builds upon previous research by simultaneously considering five objectives in DMA design (i.e. design cost, operational cost, RI, average pressure and water age) using the NSGAIII optimisation tool in pumped water networks. In this extended approach, the pump's role in meeting nodal demands eliminates the necessity of including demand shortfall as one of the objectives. The proposed methodology has been evaluated on two benchmark networks, demonstrating its capability to identify DMA alternatives and provide solutions based on user preferences. Finally, the obtained results are compared with the previous study's findings.
Discovering communities is crucial for studying the structure and dynamics of networks. Groups of related nodes in the community often correspond to functional subunits such as protein complexes or social spheres. The...
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ISBN:
(纸本)9783642543708;9783642543692
Discovering communities is crucial for studying the structure and dynamics of networks. Groups of related nodes in the community often correspond to functional subunits such as protein complexes or social spheres. The modularity optimization method is typically an effective algorithm with global objective function. In this paper, we attempt to further enhance the quality of modularity optimization by mining local close-knit structures. First, both periphery and core close-knit structures are defined, and several fast mining and merging algorithms are presented. Second, a novel fastnewman (FN) algorithm named NFN incorporating local structures into global optimization is proposed. Experimental results in terms of both internal and external on six real-world social networks have demonstrated the effectiveness of NFN on community detection.
Community detection is one of the most popular issues in analyzing and understanding the networks. Existing works show that community detection can be enhanced by proper assignments of weights onto the edges of a netw...
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ISBN:
(纸本)9781479986965
Community detection is one of the most popular issues in analyzing and understanding the networks. Existing works show that community detection can be enhanced by proper assignments of weights onto the edges of a network. Large numbers of edge weighting schemes have been developed to cope with this problem. However, hardly has a satisfied balance between the local and global weightings been found. In this paper, the problem of the local and global weighting balance is first proposed and discussed. The SimRank is next introduced as a novel edge weighting method. Furthermore, the fast newman algorithm is extended to be applicable for a weighted network. Combined with the edge weighting techniques, the extended algorithm enhances the performance of the original algorithm significantly through exhaustive experiments. And by comparing with several weighting methods, the experiments demonstrate that the proposed algorithm is superior and more robust for different kinds of networks.
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