无人机路径规划:一个双人群合作的蜂蜜子算法,用于分阶段融合多个差异化的进化策略
Xiaojie Tang1, Chengfen Jia1, Zhengyang He1
1School of Mechanical Engineering, Sichuan University Jinjiang College, Meishan 620860, China.
Biomimetics (Basel, Switzerland)
|March 26, 2025
概括
本研究引入了一种增强的蜂算法 (LRMHBA),以提高无人机3D路径规划效率,避免过早的融合. 在复杂的环境和基准测试中,LRMHBA实现了卓越的性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 无人机3D路径规划的现有算法面临着低优化效率和过早融合的挑战.
- 在现实场景中复杂的操作约束加剧了这些限制.
研究的目的:
- 为高效准确的无人机3D路径规划提出一个增强的蜂算法 (LRMHBA).
- 为了提高全球搜索效率,并在复杂的操作环境中逃避局部最佳.
主要方法:
- 构建了一个3D地形模型,包括威胁源和无人机限制.
- 综合拉丁超立方样本和精英人口战略,以优化最初的人口分布.
- 引入了随机扰动机制和差异突变策略,采用两种种群的合作方法.
主要成果:
- 在CEC2017基准套件上的11个比较算法中,LRMHBA表现出优异的性能.
- 在无人机3D路径规划中始终生成最短的平均路径,跨越不同复杂度的障碍物.
- 在弗里德曼测试中的路径规划有效性中获得最高排名.
结论:
- 拟议的LRMHBA有效地解决了无人机3D路径规划现有算法的局限性.
- LRMHBA提供了增强的勘探-开发平衡,从而提高了融合准确性和路径最佳性.
- 该算法显示了现实世界无人机运营应用的巨大潜力.
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