A comprehensive survey of sine cosine algorithm: variants and applications

Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems i...

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Veröffentlicht in:The Artificial intelligence review Jg. 54; H. 7; S. 5469 - 5540
Hauptverfasser: Gabis, Asma Benmessaoud, Meraihi, Yassine, Mirjalili, Seyedali, Ramdane-Cherif, Amar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Netherlands 01.10.2021
Springer
Springer Nature B.V
Springer Verlag
Schlagworte:
Algorithms
Artificial Intelligence
Attention
Classification
Computer Science
Convergence
Heuristic
Heuristic methods
Image classification
Image processing
Mathematical optimization
Optimization
Path planning
Radial distribution
Trigonometric functions
Variants
Sine Cosine Algorithm
Population-based Algorithm
Optimization
Meta-heuristics
ISSN:0269-2821, 1573-7462
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.
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PMCID: PMC8171367
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-021-10026-y