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

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.

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Publication Details

Journal
Journal Of Big Data
Published
2026-04-26
DOI
https://doi.org/10.1186/s40537-026-01427-3
Primary Topic
Robotic Path Planning Algorithms
Type
article
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article

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

Jiehong Wu, Jiatong Liu, Mingbin Wang, Xianchang Wang et al.
Journal Of Big Data
Robotic Path Planning Algorithms
article

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

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

Abstract

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.

Journal Of Big Data
Shenyang Aerospace University (CN), Wenzhou University (CN), University of Tehran (IR), Jilin Agricultural University (CN)
Sustainable cities and communities
Openalex Percentile: Top 4%
Robotic Path Planning Algorithms
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Artemisinin optimization with interactive mechanism and enhanced solution quality for 3D UAV path planning in complex low-altitude environments — Jiehong Wu, Jiatong Liu, et al. · Journal Of Big Data (2026) | TGRS Research Map | TGRS