Huanlai Xing
An improved MOEA/D algorithm for multi-objective multicast routing with network coding
Xing, Huanlai; Wang, Zhaoyuan; Li, Tianrui; Li, Hui; Qu, Rong
Authors
Abstract
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.
Citation
Xing, H., Wang, Z., Li, T., Li, H., & Qu, R. (2017). An improved MOEA/D algorithm for multi-objective multicast routing with network coding. Applied Soft Computing, 59, https://doi.org/10.1016/j.asoc.2017.05.033
Journal Article Type | Article |
---|---|
Acceptance Date | May 17, 2017 |
Online Publication Date | May 22, 2017 |
Publication Date | Oct 1, 2017 |
Deposit Date | Jun 21, 2017 |
Publicly Available Date | Jun 21, 2017 |
Journal | Applied Soft Computing |
Print ISSN | 1568-4946 |
Electronic ISSN | 1872-9681 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 59 |
DOI | https://doi.org/10.1016/j.asoc.2017.05.033 |
Keywords | Network coding; Multicast; Multi-objective evolutionary algorithm |
Public URL | https://nottingham-repository.worktribe.com/output/966388 |
Publisher URL | http://www.sciencedirect.com/science/article/pii/S156849461730296X |
Files
ASOC17_pszrq.pdf
(2.4 Mb)
PDF
Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0
You might also like
Models of Representation in Computational Intelligence [Guest Editorial]
(2023)
Journal Article
Automated algorithm design using proximal policy optimisation with identified features
(2022)
Journal Article
An Efficient Federated Distillation Learning System for Multitask Time Series Classification
(2022)
Journal Article
A Collaborative Learning Tracking Network for Remote Sensing Videos
(2022)
Journal Article
Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification
(2021)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: digital-library-support@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search