An improved MOEA/D algorithm for multi-objective multicast routing with network coding
Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable...
| 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/43304/ |
| _version_ | 1848796659046154240 |
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| author | Xing, Huanlai Wang, Zhaoyuan Li, Tianrui Li, Hui Qu, Rong |
| author_facet | Xing, Huanlai Wang, Zhaoyuan Li, Tianrui Li, Hui Qu, Rong |
| author_sort | Xing, Huanlai |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding.
Multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time. |
| first_indexed | 2025-11-14T19:51:29Z |
| format | Article |
| id | nottingham-43304 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:51:29Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-433042020-05-04T19:55:29Z https://eprints.nottingham.ac.uk/43304/ An improved MOEA/D algorithm for multi-objective multicast routing with network coding Xing, Huanlai Wang, Zhaoyuan Li, Tianrui Li, Hui Qu, Rong Network coding enables higher network throughput, more balanced traffic, and securer data transmission. However, complicated mathematical operations incur when packets are combined at intermediate nodes, which, if not operated properly, lead to very high network resource consumption and unacceptable delay. Therefore, it is of vital importance to minimize various network resources and end-to-end delays while exploiting promising benefits of network coding. Multicast has been used in increasingly more applications, such as video conferencing and remote education. In this paper the multicast routing problem with network coding is formulated as a multi-objective optimization problem (MOP), where the total coding cost, the total link cost and the end-to-end delay are minimized simultaneously. We adapt the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for this MOP by hybridizing it with a population-based incremental learning technique which makes use of the global and historical information collected to provide additional guidance to the evolutionary search. Three new schemes are devised to facilitate the performance improvement, including a probability-based initialization scheme, a problem-specific population updating rule, and a hybridized reproduction operator. Experimental results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art MOEAs regarding the solution quality and computational time. Elsevier 2017-10 Article PeerReviewed Xing, Huanlai, Wang, Zhaoyuan, Li, Tianrui, Li, Hui and Qu, Rong (2017) An improved MOEA/D algorithm for multi-objective multicast routing with network coding. Applied Soft Computing, 59 . pp. 88-103. ISSN 1872-9681 Network coding; Multicast; Multi-objective evolutionary algorithm http://www.sciencedirect.com/science/article/pii/S156849461730296X doi:10.1016/j.asoc.2017.05.033 doi:10.1016/j.asoc.2017.05.033 |
| spellingShingle | Network coding; Multicast; Multi-objective evolutionary algorithm Xing, Huanlai Wang, Zhaoyuan Li, Tianrui Li, Hui Qu, Rong An improved MOEA/D algorithm for multi-objective multicast routing with network coding |
| title | An improved MOEA/D algorithm for multi-objective multicast routing with network coding |
| title_full | An improved MOEA/D algorithm for multi-objective multicast routing with network coding |
| title_fullStr | An improved MOEA/D algorithm for multi-objective multicast routing with network coding |
| title_full_unstemmed | An improved MOEA/D algorithm for multi-objective multicast routing with network coding |
| title_short | An improved MOEA/D algorithm for multi-objective multicast routing with network coding |
| title_sort | improved moea/d algorithm for multi-objective multicast routing with network coding |
| topic | Network coding; Multicast; Multi-objective evolutionary algorithm |
| url | https://eprints.nottingham.ac.uk/43304/ https://eprints.nottingham.ac.uk/43304/ https://eprints.nottingham.ac.uk/43304/ |