在复杂的约束条件下,多策略混合适应性虫优化用于无人机摄像度3D路径规划
Muyang Wu1, Guo Li1, Jinwei Liao1
1College of Information and Intelligence Engineering, Zhejiang Wanli University, Ningbo, 315100, China.
Scientific reports
|April 29, 2025
概括
一个新的算法,MSDBO,通过增强全球搜索和避免过早的融合,改进了三维无人机 (UAV) 路径规划. 这导致在复杂环境中更高效,更强大的飞行路径优化.
科学领域:
- 机器人和自动化机器人与自动化
- 人工智能和优化的优化
- 航空航天工程 航空航天工程
背景情况:
- 三维无人机 (UAV) 路径规划是一个高维优化问题,需要强大的全球搜索能力.
- 传统的优化方法往往遭受过早的融合和有限的本地搜索效率.
- 现有的算法很难有效地在UAV复杂的环境中导航.
研究的目的:
- 提出和评估一种新的多策略融合算法,MSDBO,用于增强3D无人机路径规划.
- 解决过早融合的局限性,提高无人机路径查找中的本地搜索效率.
- 在复杂的城市飞行场景中证明算法的有效性.
主要方法:
- 开发了MSDBO,一个多策略的融合算法,结合了对人口多样性的零碎混乱映射.
- 集成的OOA (绿洲优化算法) 改进了全球勘探和动态平衡机制.
- 增强模拟化,以获得更高的收精度,利用西格合因子,自适应的t分布突变和动态重量.
主要成果:
- 与其他七个元启发算法相比,MSDBO在21个基准函数,Wilcoxon测试和CEC2021中展示了卓越的融合准确性和稳定性.
- 城市飞行实验表明,MSDBO产生了更光滑的路径,实现了比标准DBO低7.5%的最佳成本和31%的标准偏差.
- 协调的多阶段优化方法在复杂的无人机路径规划场景中被证明是有效的.
结论:
- 在3D无人机路径规划的融合精度和稳定性方面,MSDBO显著优于现有方法.
- 拟议的算法有效地应对复杂环境中的挑战,为无人机操作提供实际优势.
- 在优化无人机飞行路径以提高效率和可靠性方面,MSDBO是一个有希望的进步.
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