A Quantum Tunneling and Bio-Phototactic Driven Enhanced Dwarf Mongoose Optimizer for UAV Trajectory Planning and Engineering Problem
Mingyang Yu, Haorui Yang, Kangning An, Xinjian Wei, Xiaoxuan Xu, Jing Xu
随着无人机(UAV)的广泛采用,有效的路径规划变得越来越重要。 尽管传统的搜索方法已经被广泛应用,但元归信算法由于其效率和特定问题的新见而广受欢迎。 然而,过早趋同和缺乏解决方案多样性等挑战仍然阻碍了其在复杂情况下的表现。 为了解决这些问题,本文提出了增强的多策略矮人Mongoose优化(EDMO)算法,该算法专为动态和障碍物丰富的环境中的三维无人机轨迹规划而设计。 EDMO集成了三种新策略:(1)动态量子隧道优化策略(DQTOS),使粒子能够概率地逃逸局部视点;(2)生物光子学动态聚焦搜索策略(BDFSS)以微生物光轴为灵感,用于自适应局部细化;(3)正交镜头反对学习(OLOBL)策略,通过结构化尺寸重组增强全球探索。 EDMO以CEC2017和CEC2020的39个标准测试功能为基准测试,在收敛速度,稳健性和优化精度方面优于14种高级算法。 此外,对无人机三维路径规划和三个工程设计任务进行的实际验证证实了其在需要智能,适应性和时间效率规划的现场机器人任务中的实际应用性和有效性。
With the widespread adoption of unmanned aerial vehicles (UAV), effective path planning has become increasingly important. Although traditional search methods have been extensively applied, metaheuristic algorithms have gained popularity due to their efficiency and problem-specific heuristics. However, challenges such as premature convergence and lack of solution diversity still hinder their performance in complex scenarios. To address these issues, this paper proposes an Enhanced Multi-Strategy...
