Abstract: The solution of path planning problem has theoretical and practical application value. In order to find the shortest path and solve the problems of the traditional algorithm, such as the slow convergence rate, the low precision of optimization and the fact that the global optimal value is easy to fall into the local optimal solution region, this paper proposes an improved salp swarm algorithm based on the disturbance factor and the adaptive inertia weight (DISSA).Firstly, a disturbance factor was added in the updating stage of the leader position to expand the search range to improve the local search ability. By guiding individuals to explore other positions, the diversity of the population was increased.Secondly, the follower's position update is improved by replacing the previous generation's optimal position with the previous generation's position, so as to solve the problem of follower's blind following, and further strengthen the local search ability of the algorithm.Then the inertia weight controlled by hyperbolic tangent function is introduced in the improved follower position update stage to balance the global search and local search capabilities of the algorithm.By selecting 12 benchmark test functions for simulation experiments, and comparing with Salp Swarm Optimization (SSA), Particle Swarm Optimization (PSO), Ant Lion Optimization (ALO) and Sooty Tern Optimization Algorithm (STOA), the experimental results show that the proposed algorithm can effectively accelerate the convergence speed and improve the optimization accuracy. Finally, the improved algorithm is applied to the path planning problem, and the results show that the proposed algorithm can find a better path than other algorithms.