Zhu Xuemei, Jia Chaochuan, Zhao Jiangdong, Xia Chunyang, Peng Wei, Huang Ji, Li Ling
Experimental Training Teaching Management Department, West Anhui University, Yu'an District, Lu'an 237012, China.
School of Electronics and Information Engineering, West Anhui University, Yu'an District, Lu'an 237012, China.
Biomimetics (Basel). 2025 Jun 6;10(6):377. doi: 10.3390/biomimetics10060377.
This paper presents an enhanced artificial lemming algorithm (EALA) for solving complex unmanned aircraft system (UAV) path planning problems in three-dimensional environments. Key improvements include chaotic initialization, adaptive perturbation, and hybrid mutation, enabling a better exploration-exploitation balance and local refinement. Validation on the IEEE CEC2017 and CEC2022 benchmark functions demonstrates the EALA's superior performance, achieving faster convergence and better algorithm performance compared to the standard ALA and 10 other algorithms. When applied to UAV path planning in large- and medium-scale environments with realistic obstacle constraints, the EALA generates Pareto-optimal paths that minimize length, curvature, and computation time while guaranteeing collision avoidance. Benchmark tests and realistic simulations show that the EALA outperforms 10 algorithms. This method is particularly suited for mission-critical applications with strict safety and time constraints.
本文提出了一种增强型人工旅鼠算法（EALA），用于解决三维环境中复杂的无人机系统（UAV）路径规划问题。关键改进包括混沌初始化、自适应扰动和混合变异，能够实现更好的探索-利用平衡和局部优化。在IEEE CEC2017和CEC2022基准函数上的验证表明，EALA具有卓越的性能，与标准ALA和其他10种算法相比，收敛速度更快，算法性能更好。当应用于具有实际障碍物约束的大中型环境中的无人机路径规划时，EALA生成帕累托最优路径，在保证避碰的同时，使路径长度、曲率和计算时间最小化。基准测试和实际模拟表明，EALA优于10种算法。该方法特别适用于具有严格安全和时间约束的关键任务应用。
