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. 2021;54(7):5469-5540.
doi: 10.1007/s10462-021-10026-y. Epub 2021 Jun 2.

A comprehensive survey of sine cosine algorithm: variants and applications

Asma Benmessaoud Gabis  1 , Yassine Meraihi  2 , Seyedali Mirjalili  3 , Amar Ramdane-Cherif  4
Affiliations

A comprehensive survey of sine cosine algorithm: variants and applications

Asma Benmessaoud Gabis et al. Artif Intell Rev. 2021.

Abstract

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.

Keywords: Meta-heuristics; Optimization; Population-based Algorithm; Sine Cosine Algorithm.

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Figures

Fig. 1
Fig. 1
Classification of meta-heuristic algorithms
Fig. 2
Fig. 2
Number of SCA related publications by scientific databases
Fig. 3
Fig. 3
Number of publications on SCA per year
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Fig. 4
Top 10 countries ranked by number of publications on the SCA algorithm
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Fig. 5
Top 10 SCA-related keywords
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Fig. 6
The effects of sine cosine functions in Eqs. (1) and (2) on the next position (Mirjalili 2016b)
Fig. 7
Fig. 7
Flowchart of the Sine Cosine Algorithm
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Fig. 8
Variants of SCA
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Fig. 9
Modified versions of SCA
Fig. 10
Fig. 10
The modified versions of SCA
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Fig. 11
Hybridization versions of SCA
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Fig. 12
The hybridized versions of SCA
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Fig. 13
The applications of SCA
See this image and copyright information in PMC

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