A Near-Optimum Parallel Algorithm for a Graph Layout Problem(Neural Networks and Bioengineering)
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概要
- 論文の詳細を見る
We present a learning algorithm of the Hop field neural network for minimizing edge crossings in linear drawings of nonplanar graphs. The proposed algorithm uses the Hop field neural network to get a local optimal number of edge crossings, and adjusts the balance between terms of the energy function to make the network escape from the local optimal number of edge crossings. The proposed algorithm is tested on a variety of graphs including some "real word" instances of interconnection networks. The proposed learning algorithm is compared with some existing algorithms. The experimental results indicate that the proposed algorithm yields optimal or near-optimal solutions and outperforms the compared algorithms.
- 社団法人電子情報通信学会の論文
- 2004-02-01
著者
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TANG Zheng
Faculty of Engineering, Toyama University
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WANG Rong-Long
Faculty of Engineering, Fukui University
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Wang Rong-long
Faculty Of Engineering Toyama University
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Wang Rong-long
Faculty Of Engineering University Of Fukui
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Tang Zheng
Faculty Of Engineering Miyazaki University
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Xu Xin-shun
Faculty Of Engineering Toyama University
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Wang Rong-long
Faculty Of Engineering Fukui University
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