In this paper, a hybrid algorithm called rough sine cosine algorithm (RSCA) is introduced for solving engineering optimization problems by merging the sine cosine algorithm (SCA) with the rough set theory concepts (RST). RSCA combines the benefits of SCA and RST to focus the search for a promising region where the global solution can be found. Due to imprecise information on the optimization problems, efficient algorithms roughly identify the optimal solution for this type of uncertain data. The fundamental motive for adding the RST is to deal with the imprecision and roughness of the available information regarding the global optimal, especially for large dimensional problems. The cut concept of RST targeted the more interesting search region so the optimal operation could be sped up, and the global optimum could be reached at a low computational cost. The proposed RSCA algorithm is tested on 23 benchmark functions and 3 design problems. RSCA’s obtained results are mainly compared to the SCA, which is used as a first level of the proposed algorithm in this work and those of other algorithms in the literature. According to the comparisons, the RSCA can provide very competitive performance with different algorithms.

本文提出了一种称为粗糙正余弦算法（RSCA）的混合算法，通过将正弦余弦算法（SCA）与粗糙集理论概念（RST）相结合来解决工程优化问题。RSCA 结合了 SCA 和 RST 的优点，重点寻找可以找到全球解决方案的有前景的区域。由于优化问题的信息不精确，有效的算法可以粗略地确定此类不确定数据的最优解。添加RST的根本动机是为了处理关于全局最优的可用信息的不精确性和粗糙性，特别是对于大维问题。RST的剪切概念针对更有趣的搜索区域，因此可以加速优化操作，并且可以以较低的计算成本达到全局最优。所提出的 RSCA 算法在 23 个基准函数和 3 个设计问题上进行了测试。RSCA 获得的结果主要与 SCA 进行比较，SCA 用作本工作中所提出算法的第一级以及文献中其他算法的结果。根据比较，RSCA在不同的算法下都可以提供非常有竞争力的性能。