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 optimal solution, so more new techniques methods are needed solve such challenges. Metaheuristic algorithms have received a lot of attention recent years because their efficient performance simple structure. Sine Cosine Algorithm (SCA) is algorithm that based on two trigonometric functions & Cosine. However, like all other metaheuristic algorithms, SCA has slow convergence may sub-optimal regions. In this study, an enhanced version named RDSCA suggested depends techniques: random spare/replacement double adaptive weight. The first technique employed speed whereas second method used enhance exploratory searching capabilities. To evaluate RDSCA, 30 from CEC 2017 4 real-world engineering used. Moreover, nonparametric test called Wilcoxon signed-rank carried out at 5% level significance obtained results between 5 variants SCA. show competitive with metaheuristics algorithms.