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Three-Dimensional Path Planning of UAV Based on Improved Particle Swarm Optimization

Author

Listed:
  • Lixia Deng

    (School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Huanyu Chen

    (School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Xiaoyiqun Zhang

    (School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Haiying Liu

    (School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

Abstract

The traditional particle swarm optimization algorithm is fast and efficient, but it is easy to fall into a local optimum. An improved PSO algorithm is proposed and applied in 3D path planning of UAV to solve the problem. Improvement methods are described as follows: combining PSO algorithm with genetic algorithm (GA), setting dynamic inertia weight, adding sigmoid function to improve the crossover and mutation probability of genetic algorithm, and changing the selection method. The simulation results show that the improved PSO algorithm solves better route results and is faster and more stable.

Suggested Citation

  • Lixia Deng & Huanyu Chen & Xiaoyiqun Zhang & Haiying Liu, 2023. "Three-Dimensional Path Planning of UAV Based on Improved Particle Swarm Optimization," Mathematics, MDPI, vol. 11(9), pages 1-13, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:1987-:d:1130603
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    References listed on IDEAS

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    1. Ioannis Giagkiozis & Robin C. Purshouse & Peter J. Fleming, 2015. "An overview of population-based algorithms for multi-objective optimisation," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(9), pages 1572-1599, July.
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