无人机三维路径规划问题是在复杂三维环境中找到起点与终点之间最优路径的组合优化问题，但大多数路径规划算法难以在可接受的时间和精度范围内找到可行路径，因此提出了一种基于K-means++聚类优化的动态多子群樽海鞘群算法用于解决上述问题。首先，在三维环境模型中结合高度成本提出新的成本函数，将路径规划问题转化为多维函数优化问题。其次，采用K-means++聚类算法对种群进行分群，并设计动态多子群机制均衡算法的全局搜索与局部开发；各子群结合多策略协同改进，在避免算法陷入局部最优的同时提高全局寻优能力。最后，在12个CEC2017基准测试函数中验证了该算法对比其他5种算法（ISSA、MSNSSA、IBSO、MBFPA、SSA）的性能后，将其应用于三维环境中对最优路径规划问题进行求解。在不同的环境模型下的仿真实验结果表明，该算法的平均有效路径率相较于其他5种算法分别提高了15.5%、11%、23%、20.5%和18%，这证实了该算法在复杂环境下具有优秀的寻优能力。

The Unmanned aerial vehicles three-dimensional path planning problem is a combinatorial optimization problem to find the optimal path between the starting point and the endpoint in complex three-dimensional environment, but most path planning algorithms struggle to find feasible paths within acceptable time and precision range, therefore, a dynamic multi-subswarm salp swarm algorithm based on K-means + + clustering optimization was proposed to address the aforementioned issue. Firstly, a new cost function incorporating height cost was proposed within the three-dimensional environment model. The path planning problem was converted into a multi-dimensional function optimization issue. Secondly, the population was clustered using the K-means + + clustering algorithm, and a dynamic multi-subswarm mechanism was designed to balance the algorithm's global search and local exploitation. Each subswarm collaborates with multiple strategies for improvement, avoiding the algorithm from being trapped in local optima while enhancing global optimization capability. Finally, after validating the algorithm against five algorithms ISSA, MSNSSA, IBSO, MBFPA, and SSA using 12 CEC2017 benchmark test functions, it was applied to solve the optimal path planning problem in three-dimensional environments. Simulation results under different environmental models demonstrate that the algorithm's average effective path rate is increased by 15.5%, 11%, 23%, 20.5% and 18% compared to the other five algorithms, confirming its excellent optimization capability in complex environments.