UAV path planning based on hybrid particle swarm and gray wolf optimization algorithm

Kaijiang Wang; Zipeng Zhao

doi:10.1117/12.3029076

22 May 2024 UAV path planning based on hybrid particle swarm and gray wolf optimization algorithm

Kaijiang Wang, Zipeng Zhao

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131762S (2024) https://doi.org/10.1117/12.3029076
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Addressing the problem of sensitivity in parameter selection within traditional particle swarm optimization (PSO), where varying parameter values can significantly impact path optimization performance, this study replaces PSO with gray wolf optimization (GWO). An improved algorithm, grounded in GWO, is proposed for static three-dimensional (3D) path planning of unmanned aerial vehicles (UAVs). The improved algorithm combines GWO and PSO. By improving the position update formula, the individual gray wolf can adjust its position more flexibly during the search process, and improve the issue that GWO is prone to stuck in local optimum prematurely when searching for optimization. Test and simulation outcomes demonstrate the improved algorithm's superior performance in path planning.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kaijiang Wang and Zipeng Zhao "UAV path planning based on hybrid particle swarm and gray wolf optimization algorithm", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131762S (22 May 2024); https://doi.org/10.1117/12.3029076

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