增强的坚果破碎机优化算法与超标的 sinus-cosine 改进用于无人机路径规划.
Shuhao Jiang1, Shengliang Cui1, Haoran Song1
1School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China.
Biomimetics (Basel, Switzerland)
|December 27, 2024
概括
一个新的算法,ISCHNOA,增强无人机 (UAV) 在复杂环境中的路径规划. 这种方法改善了最佳路径识别,并降低了飞行成本,使无人机运行更安全,更有效.
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:
- 三维 (3D) 路径规划对于在复杂环境中安全有效的无人机导航至关重要.
- 传统的算法难以避免复杂的障碍,并快速识别最佳路径.
研究的目的:
- 为无人机路径规划引入改进的优化算法.
- 在复杂的3D空间中提高路径查找的效率和准确性.
主要方法:
- 将超标的正弦-正弦直线 (sinh cosh) 优化器集成到一个坚果破碎器优化器 (NO) 框架中,创建 ISCHNOA 算法.
- 将生肖的开发过程纳入食战略,以更好地定位食物来源.
- 一个非线性函数的设计,以加速算法收.
- 引入了一个 sinh cosh优化器,使用历史位置和动态因素来改进最佳位置影响.
主要成果:
- 伊什诺亚在14个经典基准功能,CEC2014和CEC2020测试套件中表现出卓越的表现.
- 该算法已成功应用于无人机路径规划模型.
- 在路线规划效率和准确性方面,ISCHNOA显著超过了现有的算法.
结论:
- 拟议的ISCHNOA算法在无人机3D路径规划方面取得了重大进展.
- 在复杂的环境中,ISCHNOA有效地解决了传统方法的局限性.
- 该算法导致总路径成本降低,提高无人机运营效率和安全.
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