Advances in Sparrow Search Algorithm: A Comprehensive Survey

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 optimiza...

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Vydáno v:Archives of computational methods in engineering Ročník 30; číslo 1; s. 427 - 455
Hlavní autoři: Gharehchopogh, Farhad Soleimanian, Namazi, Mohammad, Ebrahimi, Laya, Abdollahzadeh, Benyamin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.01.2023
Springer Nature B.V
Témata:
Algorithms
Artificial neural networks
Engineering
Exploitation
Heuristic
Heuristic methods
Hybridization
Local optimization
Machine learning
Mathematical and Computational Engineering
Mathematical functions
Mathematical programming
Optimization
Search algorithms
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ISSN:1134-3060, 1886-1784, 1886-1784
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Shrnutí: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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ISSN:1134-3060
1886-1784
1886-1784
DOI:10.1007/s11831-022-09804-w