SSA or Salp Swarm Algorithm is an effective meta-heuristic approach inspired by how Salp move together in the ocean. Numerous optimization issues have been successfully resolved using this technique in a variety of fields, including energy management, machine learning, wireless networking, and image processing. Using unimodal and multimodal benchmark functions from prior studies, we analyzed SSA’s performance against Sine Cosine Algorithm or SCA. To check the efficiency of these two algorithms, we conduct two main tests. First, we evaluate average and standard deviation using 13 popular function at dimension 20, 40, 60 and then we evaluated SSA’s and SCA’s convergence rate at dimension 60.The result indicates that in order to avoid local optima and guarantee more consistent and effective optimization Salp Swarm Algorithm performs better compare to the Sine Cosine Algorithm.