A hybrid EDA for load balancing in multicast with network coding
Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) is disseminated in a communications network, with l...
| Main Authors: | , , , , , |
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| Format: | Article |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/43677/ |
| _version_ | 1848796742153142272 |
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| author | Xing, Huanlai Li, Saifei cui, Yunhe Yan, Lianshan Pan, Wei Qu, Rong |
| author_facet | Xing, Huanlai Li, Saifei cui, Yunhe Yan, Lianshan Pan, Wei Qu, Rong |
| author_sort | Xing, Huanlai |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) is disseminated in a communications network, with load balancing considered as an important factor. To this end, a hybridized estimation of distribution algorithm (EDA) is proposed, where two novel schemes are integrated into the population based incremental learning (PBIL) framework to strike a balance between exploration and exploitation, thus enhance the efficiency of the stochastic search. The first scheme is a bi-probability-vector coevolution scheme, where two probability vectors (PVs) evolve independently with periodical individual migration. This scheme can diversify the population and improve the global exploration in the search. The second scheme is a local search heuristic. It is based on the problem-specific domain knowledge and improves the NCM transmission plan at the expense of additional computational time. The heuristic can be utilized either as a local search operator to enhance the local exploitation during the evolutionary process, or as a follow-up operator to improve the best-so-far solutions found after the evolution. Experimental results show the effectiveness of the proposed algorithms against a number of existing evolutionary algorithms. |
| first_indexed | 2025-11-14T19:52:48Z |
| format | Article |
| id | nottingham-43677 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:52:48Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-436772020-05-04T19:55:27Z https://eprints.nottingham.ac.uk/43677/ A hybrid EDA for load balancing in multicast with network coding Xing, Huanlai Li, Saifei cui, Yunhe Yan, Lianshan Pan, Wei Qu, Rong Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) is disseminated in a communications network, with load balancing considered as an important factor. To this end, a hybridized estimation of distribution algorithm (EDA) is proposed, where two novel schemes are integrated into the population based incremental learning (PBIL) framework to strike a balance between exploration and exploitation, thus enhance the efficiency of the stochastic search. The first scheme is a bi-probability-vector coevolution scheme, where two probability vectors (PVs) evolve independently with periodical individual migration. This scheme can diversify the population and improve the global exploration in the search. The second scheme is a local search heuristic. It is based on the problem-specific domain knowledge and improves the NCM transmission plan at the expense of additional computational time. The heuristic can be utilized either as a local search operator to enhance the local exploitation during the evolutionary process, or as a follow-up operator to improve the best-so-far solutions found after the evolution. Experimental results show the effectiveness of the proposed algorithms against a number of existing evolutionary algorithms. Elsevier 2017-10 Article PeerReviewed Xing, Huanlai, Li, Saifei, cui, Yunhe, Yan, Lianshan, Pan, Wei and Qu, Rong (2017) A hybrid EDA for load balancing in multicast with network coding. Applied Soft Computing, 59 . pp. 363-377. ISSN 1872-9681 Estimation of distribution algorithm; Load balancing; Multicast; Network coding; Population based incremental learning http://www.sciencedirect.com/science/article/pii/S1568494617303460 doi:10.1016/j.asoc.2017.06.003 doi:10.1016/j.asoc.2017.06.003 |
| spellingShingle | Estimation of distribution algorithm; Load balancing; Multicast; Network coding; Population based incremental learning Xing, Huanlai Li, Saifei cui, Yunhe Yan, Lianshan Pan, Wei Qu, Rong A hybrid EDA for load balancing in multicast with network coding |
| title | A hybrid EDA for load balancing in multicast with network coding |
| title_full | A hybrid EDA for load balancing in multicast with network coding |
| title_fullStr | A hybrid EDA for load balancing in multicast with network coding |
| title_full_unstemmed | A hybrid EDA for load balancing in multicast with network coding |
| title_short | A hybrid EDA for load balancing in multicast with network coding |
| title_sort | hybrid eda for load balancing in multicast with network coding |
| topic | Estimation of distribution algorithm; Load balancing; Multicast; Network coding; Population based incremental learning |
| url | https://eprints.nottingham.ac.uk/43677/ https://eprints.nottingham.ac.uk/43677/ https://eprints.nottingham.ac.uk/43677/ |