Aiming at the instability and low convergence precision of evolutionary algorithms in UAV path planninga Refracted Sparrow Search Algorithm (RSSA) is proposed.FirstlyRSSA uses Refracted Opposite-Based Learning (ROBL) to expand the search range and develop a more concealed spaceso as to enhance the global search ability of the algorithm.Thenthe crazy operator is used to make the search of the algorithm more detailedand the convergence accuracy is improved.Finallythe algorithm of Simulated Annealing (SA)is integrated to refine each solutionso as to find a higher-quality solution.RSSA is compared with other five algorithms in six standard test functionsand the test results show that RSSA is reasonable and effectiveand the Wilcoxon rank test proves that RSSA has strong optimization ability.It is applied to the UAV path planning in complex terrainand the simulation results show that RSSA has the smallest variance and the lowest cost compared with other algorithms.