Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting

Liao, Gwo-Ching

doi:10.3390/en15010130

Cited by 16 publications

(11 citation statements)

References 38 publications

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“…e SSA presents the advantages of rapid convergence and excellent search accuracy in optimization problems. Recently, several studies [24][25][26] have applied an SSA to different engineering fields with good results, which also have practical assistance for our research. Compared with the model in the research [24], we concentrated on optimizing the hyperparameters of the network model for the purpose of minimizing the human influence on the network model and improving the prediction capability of the model.…”

Section: Ssa-lstm Modelmentioning

confidence: 88%

“…Recently, several studies [24][25][26] have applied an SSA to different engineering fields with good results, which also have practical assistance for our research. Compared with the model in the research [24], we concentrated on optimizing the hyperparameters of the network model for the purpose of minimizing the human influence on the network model and improving the prediction capability of the model. erefore, we set the learning rate, the time step, the number of neurons in the LSTM layer, the number of neurons in the dense layer, and the epoch number as the target optimization parameters.…”

Section: Ssa-lstm Modelmentioning

confidence: 88%

“…e experimental results demonstrate that the optimized model has high generality and efficiency. Liao [24] applied the sparrow search algorithm to optimize the weights, bias, the number of neurons in the hidden layer, and the number of iterations of the LSTM network. is approach has improved the prediction accuracy of the model and has been successfully applied to load forecasting problems with good results.…”

Section: Related Workmentioning

confidence: 99%

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Sparrow Search Algorithm-Optimized Long Short-Term Memory Model for Stock Trend Prediction

Liu

Qin

You

et al. 2022

Computational Intelligence and Neuroscience

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The long short-term memory (LSTM) network is especially suitable for dealing with time series-related problems, which has led to a wide range of applications in analyzing stock market quotations and predicting future price trends. However, the selection of hyperparameters in LSTM networks was often based on subjective experience and existing research. The inability to determine the optimal values of the parameters results in a reduced generalization capability of the model. Therefore, we proposed a sparrow search algorithm-optimized LSTM (SSA-LSTM) model for stock trend prediction. The SSA was used to find the optimal hyperparameters of the LSTM model to adapt the features of the data to the structure of the model, so as to construct a highly accurate stock trend prediction model. With the Shanghai Composite Index stock data in the last decade, the mean absolute percentage error, root mean square error, mean absolute error, and coefficient of determination between stock prices predicted by the SSA-LSTM method and actual prices are 0.0093, 41.9505, 30.5300, and 0.9754. The result indicates that the proposed model possesses higher forecasting precision than other traditional stock forecasting methods and enhances the interpretability of the network model structure and parameters.

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Section: Ssa-lstm Modelmentioning

confidence: 88%

Section: Ssa-lstm Modelmentioning

confidence: 88%

Section: Related Workmentioning

confidence: 99%

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Sparrow Search Algorithm-Optimized Long Short-Term Memory Model for Stock Trend Prediction

Liu

Qin

You

et al. 2022

Computational Intelligence and Neuroscience

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“…Swarm intelligence is an algorithm inspired by natural biota and generated by simulating the behavior laws of things or organisms in nature [1]. Due to the characteristics of fast convergence speed, easy implementation, and simple operation [2], it is widely used in industrial engineering [3,4], production scheduling [5], information communication [6] and other fields. For example, Anter et al [7] proposed a new model to identify the state of epileptic seizures.…”

mentioning

confidence: 99%

Research and Application of a New Lion Swarm Algorithm

Fang

Guo

Liu³

2022

IEEE Access

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Aiming at the defects of fast convergence speed of lion swarm algorithm and easy to fall into local optimum, a variable-speed elastic collision lion swarm algorithm (VELSO) is proposed. Firstly, the flexibility of lioness search is increased according to the variable spiral search strategy, and then the two lionesses learn the learning strategies of teaching and learning algorithms to enhance the interactive behavior of lioness hunting. Then, the improved refraction reverse learning strategy is used to increase the diversity of the population and make the individual quality of the population better. Finally, the variablespeed elastic collision strategy is used to increase the probability of the algorithm jumping out of the local optimum and improve the ability of the algorithm to obtain the optimal solution. In order to verify the effectiveness of the proposed algorithm, 16 test functions were used to test the proposed algorithm, and compared with other algorithms, which proved that the proposed algorithm is very effective. Finally, the proposed algorithm is applied to DV-Hop positioning, which verifies the feasibility and practicability of the proposed algorithm.

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“…SSA is a swarm intelligence optimization algorithm that was proposed in 2020, which can optimize the mapping relationship between the input and output variables of the prediction model 24 . SSA can efficiently optimize the weights and thresholds of BP and Elman, and improve the prediction accuracy of the model.…”

mentioning

confidence: 99%

Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model

Chen³

et al. 2022

Sci Rep

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With the rise of machine learning, a lot of excellent algorithms are used for settlement prediction. Backpropagation (BP) and Elman are two typical algorithms based on gradient descent, but their performance is greatly affected by the random selection of initial weights and thresholds, so this paper chooses Sparrow Search Algorithm (SSA) to build joint model. Then, two sets of land subsidence monitoring data generated during the excavation of a foundation pit in South China are used for analysis and verification. The results show that the optimization effect of SSA on the gradient descent model is remarkable and the stability of the model is improved to a certain extent. After that, SSA is compared with GA and PSO algorithms, and the comparison shows that SSA has higher optimization efficiency. Finally, select SSA-KELM, SSA-LSSVM and SSA-BP for further comparison and it proves that the optimization efficiency of SSA for BP is higher than other kind of neural network. At the same time, it also shows that the seven influencing factors selected in this paper are feasible as the input variables of the model, which is consistent with the conclusion drawn by the grey relational analysis.

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Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting

Cited by 16 publications

References 38 publications

Sparrow Search Algorithm-Optimized Long Short-Term Memory Model for Stock Trend Prediction

Sparrow Search Algorithm-Optimized Long Short-Term Memory Model for Stock Trend Prediction

Research and Application of a New Lion Swarm Algorithm

Multi-factor settlement prediction around foundation pit based on SSA-gradient descent model

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