The standard shuffled frog leaping algorithm (SFLA) for optimization has the shortcomings of low optimization accuracy and easy falling into a local convergence area. To improve its performance, this study proposes an improved SFLA (CSFLA) based on local search with a cloud model and cosine leap weight update position. First, Tent chaotic mapping and backward learning are performed to generate a population so that the population has a more uniform distribution. The area where the best individuals in the subpopulation are located is explored by taking advantage of the normal property of the cloud model. Then, the leaping step size weight based on the cosine function is introduced to other individuals in the population, which makes the weight decrease from a high level at different rates throughout the iterations to improve the global search ability of the population. Finally, CSFLA is compared with multiple optimization algorithms on different types of test functions. The results show that CSFLA has a better convergence speed and accuracy and can find the global optimal solution effectively. The improved algorithm is applied to the traveling salesman problem and proved able to find shorter routes.