Title: The actual traffic prediction method based on glowworm swarm optimisation

Authors: Ke Chen; Qirui Li; Xingtong Zhu; Liyun Zuo

Addresses: Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, Maoming, Guangdong 525000, China ' Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, Maoming, Guangdong 525000, China ' Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, Maoming, Guangdong 525000, China ' Department of Computer Science and Technology, Guangdong University of Petrochemical Technology, Maoming, Guangdong 525000, China

Abstract: In order to mitigate congestion caused by the rapid growth of computer networks, a novel traffic prediction algorithm PGS (Prediction method based on Glowworm Swarm) is proposed using the glowworm swarm optimisation method. In this algorithm, the arrival flow is regarded as glowworm swarm and the node service rate is regarded as attractiveness at first, and in order to improve the prediction accuracy, the optimal position and attractiveness are obtained with the individuals' moving operation and random flying operations. Then, a simulation with OPENT and MATLAB is conducted to research on the key factors of prediction error for PGS. Compared to the wavelet transform prediction method, the prediction error is decreased 1.08%. The result shows that PGS has better adaptability.

Keywords: congestion; prediction; accuracy; glowworm swarm.

DOI: 10.1504/IJWMC.2021.114138

International Journal of Wireless and Mobile Computing, 2021 Vol.20 No.2, pp.153 - 158

Received: 10 Jun 2020
Accepted: 11 Nov 2020
Published online: 09 Apr 2021 *

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