Population based optimization algorithms improvement using the predictive particles

Elroby, M. M. H.; Mekhamer, S. F.; Talaat, H.; Hassan, Mohamed

doi:10.11591/ijece.v10i3.pp3261-3274

Cited by 2 publications

(1 citation statement)

References 23 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a nutshell, researchers have used different optimization algorithms to construct traffic models which have higher performance. While they have sufficiently considered the drawbacks of single neural network, some limitations remain [33,34]. To build better accurate models, researchers are constantly trying out new techniques and methods.…”

Section: Other Hybrid Modelmentioning

confidence: 99%

A comprehensive review on hybrid network traffic prediction model

Shi

Leau

et al. 2021

IJECE

View full text Add to dashboard Cite

Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.

show abstract

Section: Other Hybrid Modelmentioning

confidence: 99%