@Article{cmes.2023.024247,
AUTHOR = {Abdelazim G. Hussien, Guoxi Liang, Huiling Chen, Haiping Lin},
TITLE = {A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {136},
YEAR = {2023},
NUMBER = {3},
PAGES = {2267--2289},
URL = {http://www.techscience.com/CMES/v136n3/51808},
ISSN = {1526-1506},
ABSTRACT = {Many complex optimization problems in the real world can easily fall into local optimality and fail to find
the optimal solution, so more new techniques and methods are needed to solve such challenges. Metaheuristic
algorithms have received a lot of attention in recent years because of their efficient performance and simple
structure. Sine Cosine Algorithm (SCA) is a recent Metaheuristic algorithm that is based on two trigonometric
functions Sine & Cosine. However, like all other metaheuristic algorithms, SCA has a slow convergence and may
fail in sub-optimal regions. In this study, an enhanced version of SCA named RDSCA is suggested that depends on
two techniques: random spare/replacement and double adaptive weight. The first technique is employed in SCA
to speed the convergence whereas the second method is used to enhance exploratory searching capabilities. To
evaluate RDSCA, 30 functions from CEC 2017 and 4 real-world engineering problems are used. Moreover, a nonparametric test called Wilcoxon signed-rank is carried out at 5% level to evaluate the significance of the obtained
results between RDSCA and the other 5 variants of SCA. The results show that RDSCA has competitive results with
other metaheuristics algorithms.},
DOI = {10.32604/cmes.2023.024247}
}