To enhance the search capability and convergence speed of intelligent optimization algorithms in solving path planning problems, this paper proposes an improved Snow Geese Algorithm (SGA). Firstly, the acceleration conditions of the traditional algorithm are updated by introducing a repulsion factor and an attraction factor, thereby improving the convergence speed. Secondly, to further enhance the algorithm's global search capability, a novel position update method is incorporated. Finally, an extra exploration phase is added to create an exploitation-dominant and exploration-supplementary hybrid strategy, further improving the algorithm's optimization performance. Comparative experiments demonstrate that the improved SGA algorithm exhibits superior optimization capability and enhances global search performance, increasing the search success rate by 33.33% under complex constraints.