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A multi-strategy-guided sparrow search algorithm to solve numerical optimization and predict the remaining useful life of li-ion batteries

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Abstract

In this paper, a novel optimization method is proposed based on the sparrow search algorithm, namely, multi-strategy-guided sparrow search algorithm (MGSSA). It is well-known that the basic SSA has limitations such as the slow convergence speed and vulnerability to local optimality. Surrounding these two issues, some strategies are presented in the MGSSA. Firstly, the newly introduced ring topology search strategy not only maintains the diversity of the entire population but also enhances the exploration ability of the SSA. Secondly, the proposed leader-based search strategy can improve exploitation ability of the SSA to prevent falling into local optimum as much as possible. Moreover, the coordinated learning strategy is put forward to better balance between the exploration and exploitation abilities. Finally, the MGSSA is compared with seventeen advanced algorithms on two well-known benchmark suites (i.e., CEC-2017 and CEC-2020). Meanwhile, the MGSSA-based forecasting approach is applied to predict the remaining useful life for lithium-ion batteries. The statistical results indicate that the MGSSA is a high-performance optimizer, which can not only solve the defects of the original SSA, but also obtain satisfactory solutions in both complex numerical optimization and real-world application problems.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 62273088 and 62303108 and the Program of Shanghai Sailing under Grant 21YF1401400.

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Authors and Affiliations

  1. College of Information Science and Technology, Donghua University, Shanghai, China

    Jiankai Xue, Bo Shen & Anqi Pan

  2. Engineering Research Center of Digitalized Textile and Fashion Technology, Ministry of Education, Shanghai, China

    Jiankai Xue, Bo Shen & Anqi Pan

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  1. Jiankai Xue
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  2. Bo Shen
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Contributions

JX contributed to conceptualization, methodology, software, investigation, writing-original draft. BS contributed to conceptualization, writing-review, editing, supervision, funding acquisition. AP contributed to software, validation, writing-reviewing and editing.

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Correspondence to Bo Shen.

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Xue, J., Shen, B. & Pan, A. A multi-strategy-guided sparrow search algorithm to solve numerical optimization and predict the remaining useful life of li-ion batteries. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06092-y

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