UAV trajectory planning based on fused grey wolf optimization algorithm

In order to solve the UAV path planning problem under complex threat scenarios， an improved hybrid intelligent optimization algorithm termed HGWOSCA is presented， which synthesizes the merits of the grey wolf optimizer （GWO） and the Sine Cosine algorithm （SCA）. The algorithm's performance of globe search ability and partial development precision is significantly improved through the integration of a Circle chaotic initialization strategy， a nonlinear oscillation control parameter a， and an piecewise Sine selection strategy. On this basis， a mathematical model adaptive to UAV trajectory planning is developed， and cubic spline interpolation is applied to smooth the generated trajectories， thereby their feasibility and adaptability are enhanced. Simulations are conducted in comprehensive scenarios under varied threat conditions to validate the algorithm's performance. Experimental results confirm that HGWOSCA consistently has better performance than seven benchmark metaheuristic algorithms through all test scenarios， which shows fairly good robustness and superiority.