Real-world optimization problems demand an algorithm which properly explores the search space to find a good solution to the problem. The sine cosine algorithm (SCA) is a recently developed and efficient optimization algorithm, which performs searches using the trigonometric functions sine and cosine. These trigonometric functions help in exploring the search space to find an optimum. However, in some cases, SCA becomes trapped in a sub-optimal solution due to an inefficient balance between exploration and exploitation. Therefore, in the present work, a balanced and explorative search guidance is introduced in SCA for candidate solutions by proposing a novel algorithm called the memory guided sine cosine algorithm (MG-SCA). In MG-SCA, the number of guides is decreased with increase in the number of iterations to provide a sufficient balance between exploration and exploitation. The performance of the proposed MG-SCA is analysed on benchmark sets of classical test problems, IEEE CEC 2014 problems, and four well known engineering benchmark problems. The results on these applications demonstrate the competitive ability of the proposed algorithm as compared to other algorithms.

现实世界中的优化问题需要一种算法，该算法可以正确地探索搜索空间以找到问题的良好解决方案。正弦余弦算法（SCA）是最近开发的高效优化算法，它使用三角函数正弦和余弦执行搜索。这些三角函数有助于探索搜索空间以找到最佳值。但是，在某些情况下，由于勘探与开发之间的效率低下，SCA陷入了次优解决方案中。因此，在本工作中，通过提出一种称为记忆导正弦余弦算法（MG-SCA）的新颖算法，在SCA中为候选解决方案引入了一种平衡且探索性的搜索指导。在MG-SCA中，指南的数量随着迭代次数的增加而减少，以在勘探和开发之间提供足够的平衡。MG-SCA的性能在经典测试问题，IEEE CEC 2014问题和四个众所周知的工程基准问题的基准集上进行了分析。这些应用程序上的结果证明了与其他算法相比，所提出算法的竞争能力。