A short-term traffic forecasting model based on echo state network optimized by improved fruit fly optimization algorithm

Zhang, Qingyong; Qian, Hao; Chen, Yuepeng; Lei, Deming

doi:10.1016/j.neucom.2019.02.062

Cited by 38 publications

(12 citation statements)

References 26 publications

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“…Li et al [36] established a deep belief network optimised by particle swarm optimisation algorithm to conduct day-ahead traffic flow forecasting. Zhang et al [37] put forward the echo state network optimised by improved fruit fly optimisation algorithm to finish the five-minutes forecast of traffic volume.…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing

Wan

et al. 2020

IET intell. transp. syst

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Real-time and accurate multi-lane traffic condition forecasting is of great importance to the connected and automated vehicle highway system. However, the majority of existing deep learning based traffic prediction methods focus on pursuing the precision of the methods while neglect to improve the efficiency of the methods. To achieve the high accuracy and high efficiency of multi-lane traffic flow prediction simultaneously, this study proposes a novel combination method via the integration of the clockwork recurrent neural network (CWRNN) and random forest (RF) method, which is RF-CWRNN. To the best of the authors' knowledge, this is the first time that the CWRNN is introduced to capture the temporal feature of lane-level traffic flow and make traffic speed prediction. Meanwhile, the RF method is employed to measure the temporal relevance of the traffic flow and determine the optimal input time window. To verify the performance of the RF-CWRNN method, the ground-truth data of the expressways in Beijing were utilised to carry out experiments. The results indicate that the RF-CWRNN method is superior to the baseline models in terms of accuracy and robustness. Besides, the proposed method can save plenty of training time compared with the classical long short-term memory neural network.

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Section: Deep Learning Based Methodsmentioning

confidence: 99%

Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing

Wan

et al. 2020

IET intell. transp. syst

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“…Exiting other different novel optimization methods, like improved fruit fly optimization algorithm (IFOA) [76], fisher maximization based stochastic gradient descent (FM-SGD) [77], binary grey wolf algorithm (BGWO) [78] and so on.…”

Section: Optimization Of Hyper-parametersmentioning

confidence: 99%

A Review of Designs and Applications of Echo State Networks

Sun¹,

Song²,

Hong³

et al. 2020

Preprint

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Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence tasks and have achieved state-of-the-art in wide range of applications, such as industrial, medical, economic and linguistic. Echo State Network (ESN) is simple type of RNNs and has emerged in the last decade as an alternative to gradient descent training based RNNs. ESN, with a strong theoretical ground, is practical, conceptually simple, easy to implement. It avoids non-converging and computationally expensive in the gradient descent methods. Since ESN was put forward in 2002, abundant existing works have promoted the progress of ESN, and the recently introduced Deep ESN model opened the way to uniting the merits of deep learning and ESNs. Besides, the combinations of ESNs with other machine learning models have also overperformed baselines in some applications. However, the apparent simplicity of ESNs can sometimes be deceptive and successfully applying ESNs needs some experience. Thus, in this paper, we categorize the ESN-based methods to basic ESNs, DeepESNs and combinations, then analyze them from the perspective of theoretical studies, network designs and specific applications. Finally, we discuss the challenges and opportunities of ESNs by summarizing the open questions and proposing possible future works.

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“…Since the individuals in the population can interact and share information, the SI algorithm has the characteristics of strong flexibility and fast convergence, and can provide satisfactory solutions when applied to the automatic optimization of hyperparameters. At present, the Genetic Algorithm (GA) ( Zhong et al, 2017 ), the Particle Swarm Algorithm (PSO) ( Chouikhi et al, 2017 ), the Fruit Fly Optimization Algorithm (FOA) ( Tian, 2020 ; Zhang et al, 2020 ), the Differential Evolution Algorithm (DE) ( Hu, Wang & Tao, 2021 ) and the Grey Wolf Optimizer (GWO) ( Kohli & Arora, 2018 ) algorithm have been adopted to automatically optimize hyperparameters of ESN. However, the reservoir of ESN contains many nodes and its search space is large, hence the above-mentioned algorithms are not suitable for optimizing hyperparameters with a large range of values.…”

Section: Introductionmentioning

confidence: 99%

An internet traffic classification method based on echo state network and improved salp swarm algorithm

Zhang

Sun

Tian

et al. 2022

PeerJ Computer Science

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Internet traffic classification is fundamental to network monitoring, service quality and security. In this paper, we propose an internet traffic classification method based on the Echo State Network (ESN). To enhance the identification performance, we improve the Salp Swarm Algorithm (SSA) to optimize the ESN. At first, Tent mapping with reversal learning, polynomial operator and dynamic mutation strategy are introduced to improve the SSA, which enhances its optimization performance. Then, the advanced SSA are utilized to optimize the hyperparameters of the ESN, including the size of the reservoir, sparse degree, spectral radius and input scale. Finally, the optimized ESN is adopted to classify Internet traffic. The simulation results show that the proposed ESN-based method performs much better than other traditional machine learning algorithms in terms of per-class metrics and overall accuracy.

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A short-term traffic forecasting model based on echo state network optimized by improved fruit fly optimization algorithm

Cited by 38 publications

References 26 publications

Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing

Efficient deep learning based method for multi‐lane speed forecasting: a case study in Beijing

A Review of Designs and Applications of Echo State Networks

An internet traffic classification method based on echo state network and improved salp swarm algorithm

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