A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

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 non parametric 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.

Automated summary

Many real-world complex optimization problems can easily get stuck in local optima and fail to find the optimal solution, so new techniques and methods are needed to address these challenges. Metaheuristic algorithms, such as the Sine Cosine Algorithm (SCA), have gained attention due to their efficiency and simplicity. However, SCA, like other metaheuristic algorithms, has slow convergence and may struggle in sub-optimal regions. This study proposes an enhanced version of SCA called RDSCA that utilizes random spare/replacement and double adaptive weight techniques, resulting in competitive results compared to other metaheuristic algorithms.