@inproceedings{40d8544cf0034b3b8c834e86d8f51800,
title = "Adaptive Position Updating Particle Swarm Optimization for UAV Path Planning",
abstract = "In the paper, we propose a path planning method for unmanned aerial vehicles (UAVs) based on an enhanced Particle Swarm Optimization (PSO) algorithm. To address the issue of classical PSO algorithms easily converging to local optima, we introduce a dynamic inertia weight adjustment strategy. Additionally, we incorporate Tent mapping initialization, Levy flight, and adaptive t-distribution to balance both global and local search capabilities. We validate the effectiveness of the improved algorithm through simulations on various benchmark functions and UAV path planning scenarios. Experimental results demonstrate significant enhancements in convergence speed, overall solution accuracy, and stability. Our improved PSO algorithm provides effective solutions for diverse UAV path planning problems in complex environments and lays the foundation for further research on dynamic environments and three-dimensional path planning in future.",
keywords = "Adaptive, Levy flight, Particle Swarm Optimization (PSO), path planning, unmanned aerial vehicle (UAV)",
author = "Junhao Wei and Yanzhao Gu and Law, {K. L.Eddie} and Ngai Cheong",
note = "Publisher Copyright: {\textcopyright} 2024 International Federation for Information Processing - IFIP.; 22nd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2024 ; Conference date: 21-10-2024 Through 24-10-2024",
year = "2024",
language = "English",
series = "Proceedings of the International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "124--131",
booktitle = "2024 22nd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2024",
address = "United States",
}