一个多策略增强的虫优化算法用于UGV的城市路径规划
Chenhui Wei1, Zhifang Wei1, Yanlan Li2
1School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究增强了用于无人地面车辆 (UGV) 路径规划的虫优化 (DBO) 算法. 新的ESDBO算法在复杂的城市环境中实现了更准确,更稳定的路径规划.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 计算智能和优化计算智能和优化
背景情况:
- 无人地面车辆 (UGV) 在城市环境中面临复杂的路径规划挑战.
- 泥虫优化 (DBO) 算法在UGV路径规划中很受欢迎,但其收准确性差,局部最佳性差.
- 现有的DBO方法需要增强,以便在动态的城市环境中提供强大的性能.
研究的目的:
- 提出一个多策略增强的DBO算法 (ESDBO),以克服标准DBO的局限性.
- 为了提高融合准确性,全球搜索能力和UGV路径规划的本地搜索能力.
- 验证ESDBO在生成自主导航的最佳和安全路径方面的有效性.
主要方法:
- 在人口初始化中引入正弦映射以增强解决方案多样性.
- 开发了一个适应性的信息挥发变异策略,用于动态融合-全球搜索平衡.
- 设计了一个多机制的共同进化策略,以提高本地搜索能力和稳定性.
- 进行了除实验和基准测试 (CEC2017) 用于算法验证.
- 在随机MAPF基准图上进行路径规划实验.
主要成果:
- 与标准DBO相比,ESDBO表现出优越的优化能力和趋同准确性.
- 改进的算法显示稳定性得到改善,对局部最佳的敏感性降低.
- 路线规划实验证实了ESDBO能够生成具有短长度,最小转和高安全边际的全球最佳路径的能力.
- 在不同的障碍密度和地图尺度上观察到有效的性能.
结论:
- 拟议的ESDBO算法为复杂的城市路径规划提供了与传统DBO相比的显著改进.
- ESDBO提供了一种高效可靠的解决方案,用于在具有挑战性的环境中对UGV的自主导航.
- 多策略的增强有效地解决了原始DBO算法的融合精度和局部最佳问题.
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