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author:

Xia, Tian (Xia, Tian.) [1] | Guo, Wenzhong (Guo, Wenzhong.) [2] (Scholars:郭文忠) | Chen, Guolong (Chen, Guolong.) [3] (Scholars:陈国龙)

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Abstract:

An improved particle swarm optimization (PSO) algorithm for data streams scheduling on heterogeneous cluster is proposed in this paper, which adopts transgenic operator based on gene theory and correspondent good gene fragments depend on special problem to improve algorithm's ability of local solution. Furthermore, mutation operator of genetic algorithm is introduced to improve algorithm's ability of global exploration. Simulation tests show that the new algorithm can well balance local solution and global exploration and is more efficient in the data streams scheduling. © Springer-Verlag Berlin Heidelberg 2007.

Keyword:

Clustering algorithms Computational efficiency Data transfer Genetic algorithms Optimization Problem solving Scheduling algorithms

Community:

  • [ 1 ] [Xia, Tian]College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou 350002, China
  • [ 2 ] [Guo, Wenzhong]College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou 350002, China
  • [ 3 ] [Chen, Guolong]College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou 350002, China

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ISSN: 0302-9743

Year: 2007

Volume: 4683 LNCS

Page: 393-400

Language: English

0 . 4 0 2

JCR@2005

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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