一个基于行为的混合搜索适应性灰狼优化器,用于多个无人机的合作路径规划
Zhiwen Zheng1, Hao Huang2, Chenbo Li1
1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
一个新的基于行为的混合搜索自适应灰狼优化器 (HSB-GWO) 增强了多无人机 (UAV) 的合作路径规划. 这种方法优化了在复杂环境中提高效率和安全的轨迹.
科学领域:
- 机器人和自动化 机器人和自动化
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 对多个无人机 (UAV) 的合作路径规划对于提高任务效率和安全至关重要.
- 多个无人机系统的复杂性引入了重大挑战,因为多个约束,复杂的路径规划设计.
研究的目的:
- 提出一种基于行为搜索的新型混合型自适应灰狼优化器 (HSB-GWO),以应对多无人机合作路径规划的挑战.
- 在复杂的操作场景中提高多无人机轨迹的效率,安全和质量.
主要方法:
- 开发了HSB-GWO,结合了基于学习的维度狩猎 (DLH) 战略,以增强人口多样性.
- 集成的 Aquila 探索与扩展探索和 Lévy 基于飞行的缩小探索,以丰富搜索行为并避免局部最佳.
- 实施了适应性体重调整机制,使领袖狼能够根据健康状况动态调整它们对后代的贡献.
主要成果:
- HSB-GWO在IEEE CEC 2017和2019年的基准函数上表现出卓越的表现,超过了其他七种算法.
- 使用弗里德曼测试的统计分析证实HSB-GWO在测试的算法中排名第一.
- 合作路线规划模拟表明,HSB-GWO产生高质量的多无人机轨迹,以最低成本确保安全和顺的导航.
结论:
- 拟议的HSB-GWO有效地解决了多个无人机的多限制合作路径规划挑战.
- 与现有的算法相比,HSB-GWO显著提高了搜索性能和解决方案质量.
- 该方法成功地生成了最佳轨迹,以实现安全,高效和顺的多无人机操作.
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