Open Access

ARTICLE

A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

by Abdelazim G. Hussien^1,2, Guoxi Liang³, Huiling Chen^4,*, Haiping Lin^5,*

1 Department of Computer and Information Science, Linköping University, Linköping, 4189, Sweden
2 Faculty of Science, Fayoum University, Fayoum, 63514, Egypt
3 Department of Information Technology, Wenzhou Polytechnic, Wenzhou, 325035, China
4 Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China
5 College of Information Engineering, Hangzhou Vocational & Technical College, Hangzhou, 310018, China

* Corresponding Authors: Huiling Chen. Email: ; Haiping Lin. Email:

Computer Modeling in Engineering & Sciences 2023, 136(3), 2267-2289. https://doi.org/10.32604/cmes.2023.024247

Received 27 May 2022; Accepted 23 November 2022; Issue published 09 March 2023

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.

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Keywords

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Supplementary Material

Supplementary Material File

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