The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Advances in Sparrow Search Algorithm: A Comprehensive Survey
Open accessAbstract
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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Citations
Cites
A novel swarm intelligence optimization approach: sparrow search algorithm
Xue, J., & Shen, B. (2020) · Systems Science & Control Engineering · doi:10.1080/21642583.2019.1708830 · Cited by 3,839
Abdollahzadeh, B., Gharehchopogh, F. S., & Mirjalili, S. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107408 · Cited by 1,438
Self-citationAbdollahzadeh, B., Soleimanian Gharehchopogh, F., & Mirjalili, S. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22535 · Cited by 1,057
Self-citationA comprehensive survey: Whale Optimization Algorithm and its applications
Gharehchopogh, F. S., & Gholizadeh, H. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.03.004 · Cited by 758
Self-citationFarmland fertility: A new metaheuristic algorithm for solving continuous optimization problems
Shayanfar, H., & Gharehchopogh, F. S. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.07.033 · Cited by 347
Self-citationA stochastic configuration network based on chaotic sparrow search algorithm
Zhang, C., & Ding, S. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106924 · Cited by 342
Zamani, H., Nadimi-Shahraki, M. H., & Gandomi, A. H. (2022) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2022.114616 · Cited by 287
Nadimi-Shahraki, M. H., Taghian, S., Mirjalili, S., & Faris, H. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106761 · Cited by 251
A Modified Sparrow Search Algorithm with Application in 3d Route Planning for UAV
Liu, G., Shu, C., Liang, Z., Peng, B., & Cheng, L. (2021) · Sensors · doi:10.3390/s21041224 · Cited by 247
QANA: Quantum-based avian navigation optimizer algorithm
Zamani, H., Nadimi-Shahraki, M. H., & Gandomi, A. H. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104314 · Cited by 237
Cited by
Binpeng, Z., Yang, Z., Jie, G., Yan, L., Liangcai, C., & Cunfu, H. (2025) · Journal of Power Sources · doi:10.1016/j.jpowsour.2025.236469 · Cited by 12
Zhang, R., Choi, H.-S., Jung, D., Cho, H., Anh, P. H. N., & Vu, M. T. (2024) · Applied Sciences · doi:10.3390/app142210096 · Cited by 2
Metadata
- Authors
- Gharehchopogh, F. S., Namazi, M., Ebrahimi, L., & Abdollahzadeh, B.
- Venue
- Archives of Computational Methods in Engineering
- Published
- 2022-08-22
- Volume
- 30
- Issue
- 1
- Pages
- 427-455
- ISSN
- 1134-3060 (ISSN-L)
- 1886-1784
- OpenAlex
- W4292635991
- Cited by
- 399
- References
- 53
- FWCI
- 26.58
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Gharehchopogh, F. S., Namazi, M., Ebrahimi, L., & Abdollahzadeh, B. (2023). Advances in Sparrow Search Algorithm: A Comprehensive Survey. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-022-09804-w
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