The demand of modern warfare has propelled the application of multi-UAV collaboration in the military field. To address the problem of trajectory planning for multiple UAVs in a multi-threat mountainous environment with radar, artillery, and other threats, an improved Crested Porcupine Optimizer (CPO) algorithm, namely BCPO, is proposed.To tackle the issue of population diversity, the algorithm incorporates an initialization method combining opposition-based learning and good-point set initialization, which enhances the algorithm′s traversal capability. For the development phase of the CPO algorithm, a spiral search strategy based on adaptive small perturbations is introduced to further boost the global search performance. In the exploration phase of the CPO algorithm, a mutation triangle walk strategy based on the optimal random position is added to improve the local convergence efficiency. Additionally, a L-vy flight strategy with dynamic factors is proposed to help the algorithm achieve a better balance between global search and local optimization.Simulations on the CEC2017 test functions demonstrate that the BCPO algorithm has excellent convergence speed and accuracy. In a simulated mountainous environment, the BCPO algorithm shows an average performance improvement of 8.834%, 5.776%, and 21.828% compared to the CPO, GWO and WOA, respectively. Moreover, the stability of the algorithm is significantly enhanced. This method has good application value in solving multi-UAV trajectory planning problems in complex scenarios.