A comprehensive survey of sine cosine algorithm: variants and applications

被引：74

作者：

Gabis, Asma Benmessaoud ^{[1
]}

Meraihi, Yassine ^{[2
]}

Mirjalili, Seyedali ^{[3
]}

Ramdane-Cherif, Amar ^{[4
]}

机构：

[1] Ecole Natl Super Informat, Lab Methodes Concept Syst, BP 68M, Oued Smar 16309, Alger, Algeria

[2] Univ MHamed Bougara Boumerdes, LIST Lab, Ave Independence, Boumerdes 35000, Algeria

[3] Torrens Univ Australia, Ctr Artificial Intelligence Res & Optimisat, Brisbane, Qld 4006, Australia

[4] Univ Versailles St Quentin En Yvelines, LISV Lab, 10-12 Ave Europe, F-78140 Velizy Villacoublay, France

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2021年 / 54卷 / 07期

关键词：

Sine Cosine Algorithm; Optimization; Population-based Algorithm; Meta-heuristics; META-HEURISTIC OPTIMIZATION; CENTRAL FORCE OPTIMIZATION; EXTREME LEARNING-MACHINE; SALP SWARM ALGORITHM; GLOBAL OPTIMIZATION; LOCAL SEARCH; LEVY FLIGHT; DIFFERENTIAL EVOLUTION; PARTICLE SWARM; HYBRID;

D O I：

10.1007/s10462-021-10026-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：5469 / 5540

页数：72

共 259 条

[41] Advanced orthogonal learning-driven multi-swarm sine cosine optimization: Framework and case studies [J].

Chen, Hao ;

Heidari, Ali Asghar ;

Zhao, Xuehua ;

Zhang, Lejun ;

Chen, Huiling .

EXPERT SYSTEMS WITH APPLICATIONS, 2020, 144

[42] A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems [J].

Chen, Huiling ;

Wang, Mingjing ;

Zhao, Xuehua .

APPLIED MATHEMATICS AND COMPUTATION, 2020, 369

[43] An opposition-based sine cosine approach with local search for parameter estimation of photovoltaic models [J].

Chen, Huiling ;

Jiao, Shan ;

Heidari, Ali Asghar ;

Wang, Mingjing ;

Chen, Xu ;

Zhao, Xuehua .

ENERGY CONVERSION AND MANAGEMENT, 2019, 195 :927-942

[44] Cloud model based sine cosine algorithm for solving optimization problems [J].

Cheng, Jiatang ;

Duan, Zhimei .

EVOLUTIONARY INTELLIGENCE, 2019, 12 (04) :503-514

[45]

Chouksey M, 2020, ASIAPAC SIGN INFO PR, P1177

[46] Solution of short-term hydrothermal scheduling using sine cosine algorithm [J].

Das, Sujoy ;

Bhattacharya, Aniruddha ;

Chakraborty, Ajoy Kumar .

SOFT COMPUTING, 2018, 22 (19) :6409-6427

[47] An Improved Block-Matching Algorithm Based on Chaotic Sine-Cosine Algorithm for Motion Estimation [J].

Dash, Bodhisattva ;

Rup, Suvendu .

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2018, PT III, 2018, 11141 :759-770

[48]

de Oliveira L.M., 2018, 2018 International Joint Conference on Neural Networks, IJCNN 2018, P1, DOI 10.1109/SBSE.2018.8395633

[49]

Deb S, 2015, 2015 TENTH INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT (ICDIM), P249, DOI 10.1109/ICDIM.2015.7381893

[50]

Deng, 2017, 9 INT C DIG IM PROC, V10420

← 1 2 3 4 5 6 7 8 9 10 →