混合策略改进蝗虫算法及其无人机航迹规划应用OA

To address the optimization challenges of path length,collision risk,and multi-constraint objectives in UAV path planning,and aiming at the insufficient adaptability of the classical grasshopper optimization algorithm(GOA)in UAV trajectory planning,this paper proposes the hybrid strategy grasshopper optimization algorithm(HSGOA).Its hybrid strategy serves the core objectives of trajectory planning through five mechanisms:introducing a nonlinear enhanced sine term to improve individual interaction behavior and reduce trajectory redundancy for shorter path lengths;designing a cosine adaptive factor to balance exploration and exploitation;developing a dynamic comfort distance coefficient to optimize iterative trajectories;establishing an adaptive mutation mechanism to escape local optima;and integrating an elite guidance strategy to accelerate convergence.Moreover,the optimization performance of HSGOA is verified using the CEC2017 benchmark functions,and a multi-constraint model is incorporated to explore global initial solutions,thereby resolving the issues of global optimization imbalance and short-sighted local decision-making.Experimental results indicate that HSGOA achieves better optimization accuracy and stability on CEC2017 functions.In Task 1,compared with GOA,its mean value decreases by 14.08%and the optimal value decreases by 7.09%.In Task 2,its mean value decreases by 6.41%rela-tive to 10 algorithms(including WOA,PSO,ABC,GOA,LCGOA,PCA-GOA,SGOA,PSGOA,CCGOA,and MOLGOA).In Task 3,the decrease reaches 7.41%,demonstrating a significantly enhanced cross-modal optimization capability.