一个改进的红蓝优化算法用于复杂地形中的3D无人机路径规划
Yong Xu1, Ning Xue1, Yi Zhang1
1College of Electrical and Computer Science, Jilin Jianzhu University, Changchun 130119, China.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
圆形映射过渡和加权红蓝优化器 (CTWRBMO) 通过改善人口多样性和全球搜索能力来增强无人机3D路径规划. 与现有方法相比,这种新的算法产生了更短,更安全,更顺的飞行路径.
科学领域:
- 机器人和自动化 机器人和自动化
- 人工智能和机器学习
- 计算优化计算优化
背景情况:
- 传统的红蓝优化器 (RBMO) 在人口多样性和对复杂的3D无人机路径规划的全球搜索方面面临限制.
- 现有的算法在高维路径寻找场景中扎着局部最佳和不足的探索.
研究的目的:
- 引入圆形映射过渡和加权红蓝优化器 (CTWRBMO) 进行高级的3D无人机路径规划.
- 提高RBMO算法的全球搜索能力和人口多样性.
- 为了提高无人机导航的效率,安全性和流性.
主要方法:
- 实施圆形混乱映射,以实现统一的初始人口分布,增强多样性.
- 引入了对 ε 参数的动态调整,以适应本地和全球搜索优先级.
- 整合了非线性动态加权因子 (wd) 和精英扰动机制,以改善本地勘探和最佳解决方案保留.
主要成果:
- 与比较算法相比,CTWRBMO显著提高了优化准确度和融合速度.
- 实验模拟证实CTWRBMO产生更短,更安全,更顺的无人机路径.
- 该算法有效地克服了复杂的3D路径规划中普遍存在的局部最佳问题.
结论:
- CTWRBMO为3D无人机路径规划挑战提供了强大的和高效的解决方案.
- 这些改进显著提高了全球优化能力和实际应用性.
- CTWRBMO符合先进无人机导航系统的严格工程要求.
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