Abstract: This paper proposes a weighted spider monkey algorithm (WSMO). In order to further improve the performance of the spider-monkey algorithm (SMO), a linear decreasing inertia weight is introduced into the spider monkey individual's home position in the local leaders 'and local leaders' decision-making stages. This algorithm can increase the diversity of the population in the early iteration and increase the local search capabilities in the later iteration. The experimental results of six benchmark functions show that the improved algorithm is better than the original spider monkey algorithm in terms of convergence speed, optimization accuracy and robustness, especially in the optimization of multimodal function optimization problems.