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CLC number: TN915.11
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
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Adaptive swarm-based routing in communication networks
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LÜ Yong, ZHAO Guang-zhou, SU Fan-jun, LI Xiao-run. Adaptive swarm-based routing in communication networks[J]. Journal of Zhejiang University Science A, 2004, 5(7): 867-872.
@article{title="Adaptive swarm-based routing in communication networks",
author="LÜ Yong, ZHAO Guang-zhou, SU Fan-jun, LI Xiao-run",
journal="Journal of Zhejiang University Science A",
volume="5",
number="7",
pages="867-872",
year="2004",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2004.0867"
}
%0 Journal Article
%T Adaptive swarm-based routing in communication networks
%A LÜ
%A Yong
%A ZHAO Guang-zhou
%A SU Fan-jun
%A LI Xiao-run
%J Journal of Zhejiang University SCIENCE A
%V 5
%N 7
%P 867-872
%@ 1869-1951
%D 2004
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2004.0867
TY - JOUR
T1 - Adaptive swarm-based routing in communication networks
A1 - LÜ
A1 - Yong
A1 - ZHAO Guang-zhou
A1 - SU Fan-jun
A1 - LI Xiao-run
J0 - Journal of Zhejiang University Science A
VL - 5
IS - 7
SP - 867
EP - 872
%@ 1869-1951
Y1 - 2004
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2004.0867
Abstract: Swarm intelligence inspired by the social behavior of ants boasts a number of attractive features, including adaptation, robustness and distributed, decentralized nature, which are well suited for routing in modern communication networks. This paper describes an adaptive swarm-based routing algorithm that increases convergence speed, reduces routing instabilities and oscillations by using a novel variation of reinforcement learning and a technique called momentum. Experiment on the dynamic network showed that adaptive swarm-based routing learns the optimum routing in terms of convergence speed and average packet latency.
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