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A PBIL for load balancing in network coding based multicasting

Xing, Huanlai; Xu, Ying; Qu, Rong; Xu, Lexi

Authors

Huanlai Xing

Ying Xu

Profile Image

RONG QU rong.qu@nottingham.ac.uk
Associate Professor

Lexi Xu



Abstract

One of the most important issues in multicast is how to achieve a balanced traffic load within a communications network. This paper formulates a load balancing optimization problem in the context of multicast with network coding and proposes a modified population based incremental learning (PBIL) algorithm for tackling it. A novel probability vector update scheme is developed to enhance the global exploration of the stochastic search by introducing extra flexibility when guiding the search towards promising areas in the search space. Experimental results demonstrate that the proposed PBIL outperforms a number of the state-of-the-art evolutionary algorithms in terms of the quality of the best solution obtained.

Journal Article Type Article
Publication Date Jul 12, 2016
Journal Lecture Notes in Computer Science
Electronic ISSN 0302-9743
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 9787
APA6 Citation Xing, H., Xu, Y., Qu, R., & Xu, L. (2016). A PBIL for load balancing in network coding based multicasting. Lecture Notes in Artificial Intelligence, 9787, https://doi.org/10.1007/978-3-319-42108-7_3
DOI https://doi.org/10.1007/978-3-319-42108-7_3
Keywords Load balancing, Multicast, Network coding, Population based
incremental learning
Publisher URL http://link.springer.com/chapter/10.1007%2F978-3-319-42108-7_3
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information The final publication is available at link.springer.com. The final authenticated version is available online at https://doi.or
g/ 10.1007/978-3-319-42108-7_3.

Computational Science and Its Applications : ICCSA 2016 :
16th International Conference, Beijing, China, July 4-7, 2016, Proceedings, Part II. ISBN 978-3-319-42107-0.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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