Abstract: An improved butterfly optimization algorithm is proposed to address the drawbacks of slow convergence speed and easy trapping in local optima when solving the shortest path in complex environment path planning processes. First, when initializing the butterfly population, to ensure the diversity of the initial population and avoid falling into local optima, the position of the initial population was generated through a Tent mapping. Second, a dynamic sensory mode was introduced in the butterfly fragrance calculation stage, and as the iterative process continues, the butterfly's fragrance value was gradually enhanced to shorten the convergence time. Furthermore, to further shorten the convergence time, a selection factor in genetic algorithm was introduced in the global search stage to accelerate the butterfly's movement towards the optimal butterfly during the global search. Additionally, dynamic mutation factors were introduced in the local search stage to effectively avoid falling into local optima during path planning. Finally, this study employed an initial population generation strategy based on LOS detection method to further reduce the generation of path interruption points while ensuring the diversity of feasible solutions generated by the BOA algorithm. The experimental results demonstrate that the improved butterfly optimization algorithm has a faster convergence speed, and the planned path has higher smoothness while ensuring a reasonable path length.