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 Survey Survey Article
ISSN:	1134-3060, 1886-1784, 1886-1784
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

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.
AbstractList	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. 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.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.
Author	Gharehchopogh, Farhad Soleimanian Namazi, Mohammad Abdollahzadeh, Benyamin Ebrahimi, Laya
Author_xml	– sequence: 1 givenname: Farhad Soleimanian orcidid: 0000-0003-1588-1659 surname: Gharehchopogh fullname: Gharehchopogh, Farhad Soleimanian email: bonab.farhad@gmail.com organization: Department of Computer Engineering, Urmia Branch, Islamic Azad University – sequence: 2 givenname: Mohammad surname: Namazi fullname: Namazi, Mohammad organization: Department of Computer Engineering, Maybod Branch. Islamic Azad University – sequence: 3 givenname: Laya surname: Ebrahimi fullname: Ebrahimi, Laya organization: Department of Computer Engineering, Urmia Branch, Islamic Azad University – sequence: 4 givenname: Benyamin surname: Abdollahzadeh fullname: Abdollahzadeh, Benyamin organization: Department of Computer Engineering, Urmia Branch, Islamic Azad University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/36034191$$D View this record in MEDLINE/PubMed
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Snippet	Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by...
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SubjectTerms	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 Survey Survey Article
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Title	Advances in Sparrow Search Algorithm: A Comprehensive Survey
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