文献类型：期刊文献

英文题名：A new generalized cellular automata approach to optimization of fast packet switching

作者：Shuai, Dianxun[1,2]; Zhao, Hongbin[1]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci, Shanghai 200237, Peoples R China;[2]Tsinghua Univ, State Key Lab Intelligence Technol & Syst, Beijing 100084, Peoples R China

年份：2004

卷号：45

期号：4

起止页码：399

外文期刊名：COMPUTER NETWORKS

收录：;EI(收录号：2004258217828);WOS:【SCI-EXPANDED(收录号:WOS:000222046800002)】；

语种：英文

外文关键词：computer networks; fast packet switching; cellular neural network; generalized cellular automata

摘要：The optimization of fast packet switching (FPS) in computer networks is of great significance for improving the network performance. This paper presents a new generalized cellular automata (GCA) approach to effectively solve the FPS optimization problem. In contrast to the Hopfield-type neural network (HNN) and cellular neural network (CNN), the proposed GCA approach is featured by the pyramid architecture that is composed of multi-granularity macro-cells, and by the evolutionary dynamics that involves the dynamical feedbacks among macro-cells. The GCA architecture, dynamics, algorithm and properties are discussed in the context of the FPS optimization. The analysis and simulations on the FPS optimization have shown that the GCA approach has advantages over the HNN and CNN methods in terms of the solution quality, optimal ratio, convergence speed, real-time performance, interconnection complexity, and parameter selection. (C) 2004 Elsevier B.V. All rights reserved.