Echo state network optimization using binary grey wolf algorithm

Liu, Junxiu; Sun, Tiening; Luo, Yuling; Yang, Su; Cao, Yi; Zhai, Jia

doi:10.1016/j.neucom.2019.12.069

Cited by 52 publications

(17 citation statements)

References 28 publications

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“…The outcomes reveal that the new GWO scheme outperforms the detection methods mentioned in the literature in most cases. Liu et al [65] developed a new artificial neural network and deep learning model, the binary grey wolf echo state network. The performance of the developed hybrid model was analyzed using different datasets.…”

Section: Performance Evaluation Of Modelsmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

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The effects of developing technology and rapid population growth on the environment have been expanding gradually. Particularly, the growth in water consumption has revealed the necessity of water management. In this sense, accurate flow estimation is important to water management. Therefore, in this study, a grey wolf algorithm (GWO)-based gated recurrent unit (GRU) hybrid model is proposed for streamflow forecasting. In the study, daily flow data of Üçtepe and Tuzla flow observation stations located in various water collection areas of the Seyhan basin were utilized. In the test and training analysis of the models, the first 75% of the data were used for training, and the remaining 25% for testing. The accuracy and success of the hybrid model were compared via the comparison model and linear regression, one of the most basic models of artificial neural networks. The estimation results of the models were analyzed using different statistical indexes. Better results were obtained for the GWO-GRU hybrid model compared to the benchmark models in all statistical metrics except SD at the Üçtepe station and the whole Tuzla station. At Üçtepe, the FMS, despite the RMSE and MAE of the hybrid model being 82.93 and 85.93 m3/s, was 124.57 m3/s, and it was 184.06 m3/s in the single GRU model. We achieved around 34% and 53% improvements, respectively. Additionally, the R2 values for Tuzla FMS were 0.9827 and 0.9558 from GWO-GRU and linear regression, respectively. It was observed that the hybrid GWO-GRU model could be used successfully in forecasting studies.

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Section: Performance Evaluation Of Modelsmentioning

confidence: 99%

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Kilinç¹,

Yurtsever

2022

Sustainability

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“…ESN simplifies the training task of the network, in which only the weights of the output matrix should be trained without the traditional backpropagation. e topology structure of ESN is shown in Figure 1 [33].…”

Section: Echo State Networkmentioning

confidence: 99%

Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction

Liu

Bai

Wang

et al. 2022

Computational Intelligence and Neuroscience

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The nonstationary time series is generated in various natural and man-made systems, of which the prediction is vital for advanced control and management. The neural networks have been explored in the time series prediction, but the problem remains in modeling the data’s nonstationary and nonlinear features. Referring to the time series feature and network property, a novel network is designed with dynamic optimization of the model structure. Firstly, the echo state network (ESN) is introduced into the broad learning system (BLS). The broad echo state network (BESN) can increase the training efficiency with the incremental learning algorithm by removing the error backpropagation. Secondly, an optimization algorithm is proposed to reduce the redundant information in the training process of BESN units. The number of neurons in BESN with a fixed step size is pruned according to the contribution degree. Finally, the improved network is applied in the different datasets. The tests in the time series of natural and man-made systems prove that the proposed network performs better on the nonstationary time series prediction than the typical methods, including the ESN, BLS, and recurrent neural network.

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“…In the second approach, a sigmoid function is used to squash the continuous updated position, then stochastically threshold these values to find the updated binary gray wolf position. In the next years, their work was adapted by many others such as (Liu et al, 2020) and (Devanathan et al, 2019). Manikandan et al suggested new binary modifications of GWO for choosing optimal elements subsets (Manikandan et al, 2016).…”

Section: Related Work On Binarization Of Gwomentioning

confidence: 99%

On the binarization of Grey Wolf optimizer: a novel binary optimizer algorithm

Roayaei

2021

Soft Comput

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Grey Wolf Optimizer (GWO) is a populationbased evolutionary algorithm inspired by the hunting behaviour of grey wolves. GWO, in its basic form, is a real coded algorithm, therefore, it needs modifications to deal with binary optimization problems. In this paper, we review previous works on binarization of GWO, and classify them with respect to their encoding scheme, updating strategy, and transfer function. Then, we propose a novel binary GWO algorithm (named Set-GWO), which is based on set encoding and uses set operations in its updating strategy. Experimental results on different real-world combinatorial optimization problems and different datasets, show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions, running time, and scalability.

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Echo state network optimization using binary grey wolf algorithm

Cited by 52 publications

References 28 publications

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series

Broad Echo State Network with Reservoir Pruning for Nonstationary Time Series Prediction

On the binarization of Grey Wolf optimizer: a novel binary optimizer algorithm

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