JACSM - Swarm intelligence–based metaheuristics have emerged as powerful tools for solving complex optimization problems due to their adaptability and ease of implementation. Among them, the sine–cosine algorithm (SCA) is a well-known method, but it often suffers from slow convergence and premature stagnation in local optima. To address these limitations, this study introduces a modified sine–cosine algorithm (MSCA) that incorporates an adaptive operator to achieve a better balance between global exploration and local exploitation. The proposed MSCA was extensively evaluated using 23 classical benchmark functions, categorized into unimodal, multimodal, and fixed-dimension multimodal groups. Its performance was benchmarked against several state-of-the-art algorithms, and the standard SCA. Experimental results demonstrate that MSCA consistently outperforms the competitor algorithms in terms of convergence speed, accuracy, and robustness. Furthermore, statistical validation using the Wilcoxon rank-sum test and Friedman test confirms the significant superiority and scalability of MSCA across high-dimensional search spaces. Overall, the proposed MSCA offers a reliable and effective optimization framework with strong potential for addressing diverse and large-scale real-world applications.
Ayad Ramadhan Ali (Wed,) studied this question.