To overcome the shortcomings of the standard Grasshopper Optimization Algorithm (GOA),such as low search accuracy,prone to get local optimal solution and poor stability,an improved GOA based on mutualism and hybrid mutation strategy is proposed.Firstly,a nonlinear reconstruction method for convergence factor is introduced,which balances global search and local development.An individual hybrid mutation mechanism based on Gaussian-Cauchy distribution is designed to effectively avoid local optimal solution.Then,a mutualism strategy is introduced to enhance individual diversity and improve the global optimization ability of the algorithm.Then,the cost model of UAV path planning is established,and the path planning is transformed into a multi-dimensional function optimization problem.The improved GOA is used to solve the path planning problem.The fitness of individual positions is evaluated by the objective function that comprehensively considers the threat cost and the energy consumption cost.Iterations are conducted to obtain the optimal path,and the B-spline curve is introduced to smooth the final path connected by scattered points.The experimental results show that the improved algorithm has higher search accuracy,and the path can successfully avoid all of the threat areas.The study has high reference value for path planning in the area of Internet of Vehicles (IOV).