Abstract This paper presents a new sine-cosine algorithm (SCA) that is improved by a Differential Evolution (DE) hybridization scheme to achieve maximum optimization performance. The Hybridized SCA (H-SCA– DE) algorithm enhances the convergence rate, provides an efficient balance between exploration and exploitation, and improves the overall precision. Its efficiency was confirmed experimentally on twenty-three benchmark functions, and its performance was compared with that of the basic SCA and other optimization methods. Experiments showed that H-SCA–DE provided better solution quality in thirteen out of twenty-three test cases, showing excellent performance in all but one of the test conditions. The proposed approach also exhibited higher stability and robustness in various optimization scenarios. The results imply that H-SCA–DE is a highly efficient optimization algorithm with a quicker and more consistent solution for complex realworld problems