Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Bi-Velocity Discrete Particle Swarm Optimization and Its Application to Multicast Routing Problem in Communication Networks

Authors
Shen, MeieZhan, Zhi-HuiChen, Wei-NengGong, Yue-JiaoZhang, JunLi, Yun
Issue Date
Dec-2014
Publisher
Institute of Electrical and Electronics Engineers
Keywords
Communication networks; multicast routing problem (MRP); particle swarm optimization (PSO); Steiner tree problem (STP)
Citation
IEEE Transactions on Industrial Electronics, v.61, no.12, pp 7141 - 7151
Pages
11
Indexed
SCI
SCIE
SCOPUS
Journal Title
IEEE Transactions on Industrial Electronics
Volume
61
Number
12
Start Page
7141
End Page
7151
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/116161
DOI
10.1109/TIE.2014.2314075
ISSN
0278-0046
1557-9948
Abstract
This paper proposes a novel bi-velocity discrete particle swarm optimization (BVDPSO) approach and extends its application to the nondeterministic polynomial (NP) complete multicast routing problem (MRP). The main contribution is the extension of particle swarm optimization (PSO) from the continuous domain to the binary or discrete domain. First, a novel bi-velocity strategy is developed to represent the possibilities of each dimension being 1 and 0. This strategy is suitable to describe the binary characteristic of the MRP, where 1 stands for a node being selected to construct the multicast tree, whereas 0 stands for being otherwise. Second, BVDPSO updates the velocity and position according to the learning mechanism of the original PSO in the continuous domain. This maintains the fast convergence speed and global search ability of the original PSO. Experiments are comprehensively conducted on all of the 58 instances with small, medium, and large scales in the Operation Research Library (OR-library). The results confirm that BVDPSO can obtain optimal or near-optimal solutions rapidly since it only needs to generate a few multicast trees. BVDPSO outperforms not only several state-of-the-art and recent heuristic algorithms for the MRP problems, but also algorithms based on genetic algorithms, ant colony optimization, and PSO.
Files in This Item
Appears in
Collections
COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher ZHANG, Jun photo

ZHANG, Jun
ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
Read more

Altmetrics

Total Views & Downloads

BROWSE