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. 2023;30(1):427-455.
doi: 10.1007/s11831-022-09804-w. Epub 2022 Aug 22.

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Farhad Soleimanian Gharehchopogh  1 , Mohammad Namazi  2 , Laya Ebrahimi  1 , Benyamin Abdollahzadeh  1
Affiliations

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Farhad Soleimanian Gharehchopogh et al. Arch Comput Methods Eng. 2023.

Abstract

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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Figures

Fig. 1
Fig. 1
Pseudo-code of SSA algorithm [17]
Fig. 2
Fig. 2
Flowchart of SSA algorithm [17]
Fig. 3
Fig. 3
Percentage of papers published with SSA in various journals
Fig. 4
Fig. 4
Number of SSA papers published per year
Fig. 5
Fig. 5
Review of papers belongs to the SSA algorithm
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Fig. 6
Classification of SSA methods
Fig. 7
Fig. 7
Advantages of hybridization SSA with different algorithms
Fig. 8
Fig. 8
The most critical chaotic targets in SSA
Fig. 9
Fig. 9
SSA schema on ANNs
Fig. 10
Fig. 10
SSA-BP combination flowchart [90]
Fig. 11
Fig. 11
Percentage diagram of improved SSA based on different methods
Fig. 12
Fig. 12
Percentage of SSA application in different areas of optimization
Fig. 13
Fig. 13
Percentage of SSA methods based on four different areas
See this image and copyright information in PMC

References

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