Abstract: To address the problems of insufficient initial population quality, susceptibility to local optima, and poor path smoothness in mobile robot path planning using the snow ablation optimization algorithm (SAO), a multi-strategy improved snow ablation optimization algorithm (MISAO) was proposed. By introducing an adaptive distribution-guided population initialization strategy, a Levy flight-driven global perturbation mechanism, and an adaptive elastic boundary mapping method, the algorithm's global search capability and convergence stability are effectively enhanced. Experimental results on the IEEE CEC2017 benchmark function show that compared with the original SAO, MISAO improves the average optimization accuracy by approximately 42.38%, and compared with several other comparative algorithms, MISAO improves the average optimization performance by approximately 60%, achieving a smaller standard deviation on most test functions, verifying its excellent optimization ability and stability. Furthermore, when applied to the robot path planning problem, experimental results show that compared with seven algorithms, the average length of the path planned by MISAO is reduced by approximately 22.6%, indicating that it can obtain shorter, smoother, and more stable feasible paths and verifying its effectiveness and engineering application potential.