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.Firstly, when initializing the butterfly population, in order to ensure the diversity of the initial population and avoid falling into local optima, the position of the initial population is generated through a Tent mapping.Secondly, a dynamic sensory mode is introduced in the butterfly fragrance calculation stage, and as the iterative process continues, the butterfly's fragrance value is gradually enhanced to shorten the convergence time.Furthermore, in order to further shorten the convergence time, a selection factor in genetic algorithm is introduced in the global search stage to accelerate the butterfly's movement towards the optimal butterfly during the global search.Additionally,dynamic mutation factors are introduced in the local search stage to effectively avoid falling into local optima during path planning.Finally, this study employs 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.