Dynamic UAV path planning in mountainous terrain utilizing an arithmetic optimization algorithm incorporating adaptive thermal conduction search and elite population genetic strategies

•A UAV path planning algorithm for dynamic obstacle scenes is proposed.•An improved arithmetic optimization algorithm TQGAOA is proposed.•An adaptive thermal conduction search strategy is proposed.•Validity of TQGAOA proved in 6 mountainous terrain scenes.•Ability of TQGAOA optimization is proved in...

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Vydané v:Aerospace science and technology Ročník 168; s. 110950
Hlavní autori: Wang, Wentao, Tian, Jun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Masson SAS 01.01.2026
Predmet:
Adaptive thermal conduction search strategy
Arithmetic optimization algorithm
Dynamic UAV path planning
Metaheuristic algorithm
Path planning in mountainous terrain
Dynamic UAV path planning
Adaptive thermal conduction search strategy
Arithmetic optimization algorithm
Metaheuristic algorithm
Path planning in mountainous terrain
ISSN:1270-9638
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Shrnutí:•A UAV path planning algorithm for dynamic obstacle scenes is proposed.•An improved arithmetic optimization algorithm TQGAOA is proposed.•An adaptive thermal conduction search strategy is proposed.•Validity of TQGAOA proved in 6 mountainous terrain scenes.•Ability of TQGAOA optimization is proved in CEC2017 functions. Unmanned Aerial Vehicle (UAV) is playing an increasingly vital role in application missions for mountainous terrain. The ruggedness of mountainous terrain and the presence of dynamic obstacles make UAV path planning highly challenging. The goal of dynamic UAV path planning in mountainous terrain is to design a safe, energy-efficient, and smooth path to help the UAV navigate through obstacle-laden areas, thereby ensuring the efficiency of task completion. This paper establishes a path planning model that includes multiple constraints such as energy consumption and security threats to transform the dynamic UAV path planning problem into an optimization problem that minimizes the path cost. Aiming at this optimization problem, an Arithmetic Optimization Algorithm incorporating adaptive Thermal conduction search, Quadratic interpolation and elite population Genetic strategies (TQGAOA) is proposed. The introduction of these strategies aims to enhance the exploration and exploitation performance of the algorithm in dynamic UAV path planning problem. The performance of TQGAOA is validated using the CEC2017 suite and compared with eight advanced algorithms, showing significant advantages in convergence and robustness. Comparative experiments in six mountainous terrain scenarios with dynamic obstacles show that TQGAOA can adapt flexibly to different levels of complexity, and obtain high-quality paths for UAV planning in a stable and efficient manner.
ISSN:1270-9638
DOI:10.1016/j.ast.2025.110950