An Enhanced Particle Swarm Optimization Algorithm

Gao, Xue-Yao; Sun, Liquan; Sun, Danfeng

doi:10.3923/itj.2009.1263.1268

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…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

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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.

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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

show abstract

“…Hence, in order to obtain high performance, the chaotic search is run in a small range. This search area is changed in the current optimal solution neighborhood [4].…”

Section: Search Rangementioning

confidence: 99%

Application of Chaos Embedded PSO for PID Parameter Tuning

Altınöz

Yilmaz

Weber

2014

INT J COMPUT COMMUN

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Proportional-Integral-Derivative (PID) control is the most common method applied in the industry due to its simplicity. On the other hand, due to its difficulties, parameter tuning of the PID controllers are usually performed poorly. Generally, the design objectives are obtained by adjusting the controller parameters repetitively until the desired closed-loop system performance is achieved. This allows researchers to use more advanced and even some heuristic methods to achieve the optimal PID parameters. This paper focuses on application of the chaos embedded particle swarm optimization algorithm (CPSO) for PID controller tuning, and demonstrates how to employ the CPSO method to find optimal PID parameters in details. The method is applied to optimal PID parameter tuning for three typical systems with various ordered, and comparisons with the conventional PSO and the Ziegler-Nichols methods are performed. The numerical results from the simulations verify the performance of the proposed scheme.

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Design optimization with chaos embedded great deluge algorithm

Baykasoğlu

2012

Applied Soft Computing

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An Enhanced Particle Swarm Optimization Algorithm

Cited by 9 publications

References 5 publications

A comprehensive review on hybrid network traffic prediction model

A comprehensive review on hybrid network traffic prediction model

Application of Chaos Embedded PSO for PID Parameter Tuning

Design optimization with chaos embedded great deluge algorithm

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