A PBIL for load balancing in network coding based multicasting

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

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
• Conference Name: 16th International Conference Computational Science and Its Applications (ICCSA 2016)
• End Date: Jul 7, 2016
• Acceptance Date: Apr 20, 2016
• Publication Date: Jul 12, 2016
• Deposit Date: Dec 7, 2016
• Publicly Available Date: Dec 7, 2016
• Journal: Lecture Notes in Computer Science
• Electronic ISSN: 0302-9743
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 9787
• Pages: 34-44
• DOI: https://doi.org/10.1007/978-3-319-42108-7_3
• Keywords: Load balancing, Multicast, Network coding, Population based; incremental learning
• Public URL: https://nottingham-repository.worktribe.com/output/801344
• Publisher URL: http://link.springer.com/chapter/10.1007%2F978-3-319-42108-7_3
• 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.