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<p>Path planning is vital for Unmanned Aerial Vehicle (UAV), especially in low-altitude environments. This paper proposes Interactive Mechanism and Enhanced Solution Quality-driven Artemisinin Optimization (IEAO) to address UAV path planning challenges. The Interactive Mechanism enhances population diversity through communication among individuals within the population. Furthermore, IEAO combines the Artemisinin Optimization update rule with an Enhanced Solution Quality strategy to refine solution selection, thereby enhancing convergence accuracy. The effectiveness of IEAO was evaluated using IEEE CEC2017 and IEEE CEC2019 test sets. The results show that IEAO outperforms other classical and advanced algorithms in 87.26% and 80.34% of functions, respectively. Finally, IEAO was tested in a UAV path planning model with Digital Elevation Model (DEM) data. The results show that IEAO reduces the overall cost by at least 2.74% and 3.95% compared to other classical and advanced algorithms across all scenarios. This clearly indicates that IEAO is a more effective choice for addressing UAV path planning problems.</p>

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Artemisinin optimization with interactive mechanism and enhanced solution quality for 3D UAV path planning in complex low-altitude environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月26日

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  • Chengcheng Chen,
  • Mingbin Wang,
  • Jiatong Liu,
  • Xianchang Wang,
  • Helong Yu,
  • Jiehong Wu,
  • Hua Yang,
  • Ali Asghar Heidari,
  • Huiling Chen

摘要

Path planning is vital for Unmanned Aerial Vehicle (UAV), especially in low-altitude environments. This paper proposes Interactive Mechanism and Enhanced Solution Quality-driven Artemisinin Optimization (IEAO) to address UAV path planning challenges. The Interactive Mechanism enhances population diversity through communication among individuals within the population. Furthermore, IEAO combines the Artemisinin Optimization update rule with an Enhanced Solution Quality strategy to refine solution selection, thereby enhancing convergence accuracy. The effectiveness of IEAO was evaluated using IEEE CEC2017 and IEEE CEC2019 test sets. The results show that IEAO outperforms other classical and advanced algorithms in 87.26% and 80.34% of functions, respectively. Finally, IEAO was tested in a UAV path planning model with Digital Elevation Model (DEM) data. The results show that IEAO reduces the overall cost by at least 2.74% and 3.95% compared to other classical and advanced algorithms across all scenarios. This clearly indicates that IEAO is a more effective choice for addressing UAV path planning problems.