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.
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Xia, T., Guo, W., Chen, G. (2007). An Improved Particle Swarm Optimization for Data Streams Scheduling on Heterogeneous Cluster. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_43
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DOI: https://doi.org/10.1007/978-3-540-74581-5_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74580-8
Online ISBN: 978-3-540-74581-5
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