As a nature-inspired metaheuristic algorithm, salp swarm algorithm (SSA) still suffers from low searching efficiency and easily falling into local optimum, especially when solving composite problem. In order to enhance the performance of SSA, an improved SSA equipped with sine cosine algorithm and normal cloud generator (CSCSSA) is proposed in this paper. The sine and cosine operator can prevent the salp leader from ineffective search for possible food position, and speed up the search rate of SSA. In addition, the normal cloud generator is employed to replace the position update mechanismof salp followers, and enhance the diversity by generating cloud drops around the salp leader. Comprehensive comparison of CSCSSA and seven other optimization algorithms was conducted on CEC2017 benchmark functions. The statistical results and convergence curves prove that the CSCSSA can be considered as highly competitive algorithm according to the searching efficiency, convergence accuracy and the ability of local optimum avoidance.