Improved sparrow search algorithm optimization deep extreme learning machine for lithium-ion battery state-of-health prediction

Jia, Jianfang; Yuan, Shufang; Shi, Yuanhao; Wen, Jinyu; Pang, Xiaoqiong; Zeng, Jianchao

doi:10.1016/j.isci.2022.103988

Cited by 53 publications

(20 citation statements)

References 35 publications

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“…The principle of SSA is derived from the predatory behavior of sparrows and SSA shows the characteristics of fast convergence and great optimization capability. The algorithm classifies the entire sparrow population into three categories, namely joiners grabbing for food, producers looking for food, and vigilantes detecting danger ( Jia et al, 2022 ). The algorithm first needs to initialize the population with fitness value, and the sparrow population is initialized as followings:

where l indicates the sparrows' number; p is the variable dimension;

is the value of the pth dimension of the lth sparrow.…”

Section: Methods and Datamentioning

confidence: 99%

Research on the potential for China to achieve carbon neutrality: A hybrid prediction model integrated with elman neural network and sparrow search algorithm

Yang

Liu

2023

Journal of Environmental Management

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where l indicates the sparrows' number; p is the variable dimension;

is the value of the pth dimension of the lth sparrow.…”

Section: Methods and Datamentioning

confidence: 99%

Research on the potential for China to achieve carbon neutrality: A hybrid prediction model integrated with elman neural network and sparrow search algorithm

Yang

Liu

2023

Journal of Environmental Management

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“…DV-Hop is a widely used placement strategy in the WSN [ 60 ]. Based on DV-Hop wireless sensor network positioning technology, an ISSA is supplied.…”

Section: Methods Of Ssamentioning

confidence: 99%

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Gharehchopogh

Namazi

Ebrahimi

et al. 2022

Arch Computat Methods Eng

189

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Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.

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“…Sparrow search algorithm (SSA) (Jia et al, 2022;Qi et al, 2021;Wc et al, 2021) is a new intelligent optimization algorithm based on sparrows' foraging behavior and anti predation behavior. The algorithm can optimize several super parameters of LSTM at the same time, and has strong optimization ability and convergence speed.…”

Section: Ssa Optimization Algorithmmentioning

confidence: 99%

Light intensity optimization of optical fiber stress sensor based on SSA-LSTM model

Qiao

Wang

2022

Front. Energy Res.

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In order to further improve the measurement range and accuracy of optical fiber stress sensor based on the interference between rising vortex beam and plane wave beam, a new stress demodulation model is designed. This model proposes a method to optimize the long-term and short-term memory network (LSTM) model by using sparrow search algorithm (SSA), extract the main characteristics of the influence of various variables on optical fiber stress sensor, and fit the relationship between sensor stress and beam phase difference. This method is an attempt of the deep learning model LSTM in the study of stress mediation model. There are very few related studies, and it is very necessary to fill this gap. In the experiment, the SSA-LSTM neural network is trained by using the data of stress and phase difference measured by the optical fiber stress sensor. The test results show that the mean error of SSA-LSTM neural network is less than that of LSTM neural network, which shows that the combination of SSA-LSTM model and optical fiber stress sensor can make its measurement accuracy higher, The algorithm can more effectively reduce the influence of the surrounding environment and the influence of the light source fluctuation on the measurement range and accuracy of the optical fiber sensor, and has good practical application value. It is proved that the deep learning LSTM neural network has good application value in the light intensity optimization of optical fiber stress sensor.

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Improved sparrow search algorithm optimization deep extreme learning machine for lithium-ion battery state-of-health prediction

Cited by 53 publications

References 35 publications

Research on the potential for China to achieve carbon neutrality: A hybrid prediction model integrated with elman neural network and sparrow search algorithm

Research on the potential for China to achieve carbon neutrality: A hybrid prediction model integrated with elman neural network and sparrow search algorithm

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Light intensity optimization of optical fiber stress sensor based on SSA-LSTM model

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