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Review and empirical analysis of sparrow search algorithm

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Abstract

In recent years, swarm intelligence algorithms have received extensive attention and research. Swarm intelligence algorithms are a biological heuristic method, which is widely used in solving optimization problems. The traditional swarm intelligence algorithms provide new ideas and new ways to solve some practical problems, and they have made positive progress in fields such as combinatorial optimization, task scheduling, process control, engineering prediction, and image processing. In particular, the sparrow search algorithm is a new type of group intelligence optimization algorithm inspired by the group foraging behavior to perform local and global search by imitating the foraging and anti-predation behavior of sparrows. In view of the shortcomings of the original sparrow search algorithm, such as its easy fall into local optimum, slow convergence speed, and low convergence accuracy, scholars at home and abroad have improved the sparrow search algorithm and have made practical applications in various fields. Firstly, this paper introduces the basic principle of sparrow search algorithm, analyzes the factors affecting the performance of the algorithm, further proposes the improvement strategy of the algorithm, and performs function test comparison and performance analysis with particle swarm optimization algorithm, monarch butterfly algorithm, colony spider algorithm, and pigeon swarm optimization algorithm. After that, the application and development of the sparrow search algorithm in power grid load forecasting, image processing, path tracking, wireless sensor network routing performance optimization, wireless location, and fault diagnosis are described. Finally, combined with the performance characteristics and application direction of the sparrow search algorithm, the future research and development direction of the sparrow search algorithm is prospected.

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Acknowledgements

Yinggao Yue and Li Cao contributed equally to this work and should be considered as co-first authors. This work was supported in part by the Natural Science Foundation of Zhejiang Province under Grant LY23F010002, in part by Wenzhou basic scientific research project under Grant R20210030 and Wenzhou Association for Science and Technology under Grant kjfw36 and nlts12, in part by general projects of the department of education of Zhejiang Province under Grant Y202250075, the major scientific and technological innovation projects of Wenzhou Science and Technology Plan under Grant ZG2021021, school level scientific research projects of Wenzhou University of Technology under grants ky202201 and ky202209, the teaching reform research project of Wenzhou University of Technology under grant 2022JG12, Wenzhou intelligent image processing and analysis key laboratory construction project under Grant 2021HZSY007105.

Funding

Natural Science Foundation of Zhejiang Province,LY23F010002,Yinggao Yue,Major scientific and technological innovation projects of Wenzhou Science and Technology Plan under Grant ZY2019020 and ZG2021021.

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Authors and Affiliations

  1. School of Intelligent Manufacturing and Electronic Engineering, Wenzhou University of Technology, Wenzhou, 325035, China

    Yinggao Yue, Li Cao, Minghai Xu, Shuxin Wang & Haihua Ding

  2. Intelligent Information Systems Institute, Wenzhou University, Wenzhou, 325035, China

    Dongwan Lu & Zhongyi Hu

  3. Wenzhou Key Laboratory of Intelligent Lifeline Protection and Emergency Technology for Resilient City, Wenzhou University of Technology, Wenzhou, 325035, China

    Bo Li

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Contributions

DL, ZH, MX performed the experiments. LC and YY analyzed the data and wrote the paper. BL and HD revised the manuscript. SW designed the study.

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Correspondence to Shuxin Wang.

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Yue, Y., Cao, L., Lu, D. et al. Review and empirical analysis of sparrow search algorithm. Artif Intell Rev 56, 10867–10919 (2023). https://doi.org/10.1007/s10462-023-10435-1

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