MEIAO:用于全球优化和无人机路径规划的多策略增强信息采集优化器
Yongzheng Chen1, Ruibo Sun2, Jun Zheng3
1School of Mathematics, University of Edinburgh, Edinburgh EH8 8FH, UK.
Biomimetics (Basel, Switzerland)
|November 26, 2025
概括
一个新的多策略增强信息采集优化器 (MEIAO) 改善了无人机在复杂的3D地形中的路径规划. 与现有方法相比,MEIAO提供了更有效的勘探和强大的路径生成.
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:
- 传统的路径规划在复杂的3D环境中与多重约束,多目标无人机任务作斗争.
- 标准的群集智能算法,如信息获取优化器 (IAO),在探索效率,人口多样性和高维问题的边界处理方面面临局限性.
研究的目的:
- 开发一个改进的优化算法,多策略增强信息获取优化器 (MEIAO),用于复杂的无人机路径规划.
- 解决现有算法的在高维搜索空间内的探索,多样性和边界处理方面的局限性.
主要方法:
- 纳入基于征收飞行的战略,以加强全球勘探.
- 整合一个适应差异演变运算符,以动态平衡勘探和开采.
- 实施全球指导的边界处理策略,以确保可行的路径生成.
- 对CEC2017和CEC2022基准套件的性能评估与其他八种算法相比.
主要成果:
- 在当地开采,全球勘探和对基准功能的复杂适应方面,MEIAO表现出卓越的表现.
- 该算法表现出增强的稳定性,并保持了人口多样性.
- 应用于3D山地无人机路径规划,MEIAO与IAO相比,平均路径成本降低了25.7%.
- 生成的路径更光滑,无碰撞,并更快地融合.
结论:
- 在复杂的3D环境中,MEIAO为无人驾驶飞行器运行提供了更有效,更可靠的解决方案.
- 拟议的算法有效地克服了无人机路径规划中现有的方法的局限性.
- MEIAO在路径成本,流性和融合速度方面提供了显著的改进.
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