In gear train design, traditional algorithms exhibit drawbacks such as computational complexity and low accuracy. The seagull optimization algorithm (SOA) benefits from its simple algorithmic principle, strong universality, and few parameters, and is now commonly used in engineering design problems. However, the standard SOA is prone to problems such as low optimization accuracy and slow search speed. This study proposes a hybrid strategy improved seagull optimization algorithm (WLSOA). Firstly, it utilizes a nonlinear descent strategy to enhance the exploration and development capabilities of the SOA and improve optimization accuracy. Secondly, the adaptive weight balancing of global and local search capabilities and the addition of Levy flight steps to perturb the current optimal solution are introduced to improve the ability of the algorithm to jump out of the local optimal value. The performance of WLSOA is then explored through simulation experiments on 9 classic test functions, using WLSOA, golden sine algorithm, whale optimization algorithm, particle swarm optimization algorithm, traditional seagull optimization algorithm, and the newly proposed improved seagull optimization algorithm. The results show that WLSOA has higher optimization accuracy and faster convergence speed than the other six algorithms. Finally, in gear train design, a comparison with 13 other common swarm intelligence algorithms reveals that WLSOA has a better solving ability than other algorithms.