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  • 标题:An Improved Evolutionary Algorithm for Effective Optimized Network Design Using NDDR Model
  • 本地全文:下载
  • 作者:Ezhil Joe A ; V. Karunakaran M.E
  • 期刊名称:International Journal of Communication and Computer Technologies
  • 印刷版ISSN:2278-9723
  • 出版年度:2014
  • 卷号:2
  • 期号:2
  • 出版社:IJCCTS
  • 摘要:The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(√n).
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