Flight cost.

Addressing the insufficient optimization performance in drone 3D path planning and the issues of inadequate optimization precision and tendency to fall into local optima in the existing Whale Optimization Algorithm (WOA), this paper proposes a drone 3D path planning method based on an improved Whale Optimization Algorithm (CSRD-WOA). Firstly, to enhance the search efficiency and fitness accuracy of the Whale Algorithm, the Cuckoo Search and Random Differential Strategy were introduced and compared with the traditional Particle Swarm Optimization algorithm, Whale Algorithm, and Cuckoo Search Algorithm. Experimental results demonstrate that the CSRD-WOA algorithm improves global search capabilities and prevents premature convergence, significantly enhancing optimization precision and convergence speed. Secondly, applying the CSRD-WOA algorithm to drone 3D path planning issues, the simulation results show that the CSRD-WOA algorithm can effectively manage path planning in complex terrains, showcasing its application potential in drone path planning.针对现有鲸鱼优化算法（Whale Optimization Algorithm, WOA）在无人机三维路径规划任务中存在优化性能不足、优化精度欠佳且易陷入局部最优的问题，本文提出一种基于改进鲸鱼优化算法（CSRD-WOA）的无人机三维路径规划方法。首先，为提升鲸鱼算法的搜索效率与适应度精度，引入布谷鸟搜索与随机差分策略，并将改进后的算法与传统粒子群优化算法、鲸鱼算法及布谷鸟搜索算法进行对比。实验结果表明，CSRD-WOA算法可增强全局搜索能力，避免早熟收敛，显著提升优化精度与收敛速度。其次，将CSRD-WOA算法应用于无人机三维路径规划问题，仿真结果显示该算法可有效处理复杂地形下的路径规划任务，展现出其在无人机路径规划领域的应用潜力。