Research on UAV Flight Trajectory Based on PSO and Improved PSO Algorithms

Shixuan Shen

doi:10.54254/2755-2721/2026.KA27946

Applied and Computational EngineeringOpen access

Research on UAV Flight Trajectory Based on PSO and Improved PSO Algorithms

Research Article

Open Access

Research on UAV Flight Trajectory Based on PSO and Improved PSO Algorithms

Shixuan Shen ^1*

¹ Civil Aviation University of China

^*Corresponding author: 19706133985@163.com

Published on 14 October 2025

ACE Vol.192

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 纸质出版ISBN 978-1-80590-397-0

ISBN (Online): 978-1-80590-398-7

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Abstract

With the widespread application of UAV technology in military, agricultural, logistics, and other fields, path planning, as a core technology of autonomous navigation, faces challenges from complex environmental constraints and multi-objective optimization. This paper systematically investigates the application of Particle Swarm Optimization (PSO) and its improved strategies in UAV 3D path planning. Secondly, by comparing five algorithms—traditional PSO, Variable social weight PSO, mutated particle PSO, hybrid strategy PSO, and neural network-assisted PSO—in complex terrains with varying numbers of peaks (N=5, 13, 20), their performance is evaluated. Simulation results indicate that the traditional PSO algorithm is simple and efficient but prone to premature convergence. The Variable social weight PSO excels in balancing exploration and exploitation capabilities, enhancing convergence speed. The mutated particle PSO and hybrid PSO effectively avoid local optima and demonstrate superior path quality in complex terrains. The neural network-assisted PSO incurs higher computational costs in high-dimensional complex environments and is susceptible to overfitting in simple environments. This study provides a theoretical basis for algorithm selection in different mission scenarios and proposes future directions for intelligent path planning technology development.

Keywords:

uav path planning, particle swarm optimization, intelligent algorithms, 3d trajectory optimization, convergence analysis

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References

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Cite this article

Shen,S. (2025). Research on UAV Flight Trajectory Based on PSO and Improved PSO Algorithms. Applied and Computational Engineering,192,7-15.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MCEE 2026 Symposium: Advances in Sustainable Aviation and Aerospace Vehicle Automation

ISBN: 纸质出版ISBN 978-1-80590-397-0(Print) / 978-1-80590-398-7(Online)

Editor: Ömer Burak İSTANBULLU

Conference website: https://2025.confmcee.org/kayseri.html

Conference date: 14 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.192

ISSN: 2755-2721(Print) / 2755-273X(Online)

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