Electronics Optics & Control, Volume. 31, Issue 11, 26(2024)

UAV Path Planning Based on Adaptive Osprey Optimization Algorithm

CEN Zhe, FU Qiang, and TONG Nan
Author Affiliations
  • College of Science and Technology, Ningbo University, Ningbo 315000, China
  • show less

    Aiming at the problems of low convergence accuracy and local optimization of heuristic algorithm in Unmanned Aerial Vehicle (UAV) path planning, an Adaptive Osprey Optimization Algorithm (AOOA) is proposed. Firstly, Bernoulli chaotic mapping is used to improve the population diversity effectively. Secondly, adaptive cosine factor is introduced to balance the global search ability and local development ability, and in combination with Levy flight strategy, the step size is adjusted adaptively to help the individual osprey better jump out of the local optimal. Then, the refraction reverse learning strategy is used to improve the quality of the global optimal solution, and the convergence accuracy and speed are improved to a certain extent. After that, the performance of the algorithm is compared with that of other 5 algorithms in 15 CEC2005 test functions, and the results show that the algorithm has excellent performance in convergence accuracy and stability. Finally, it is transplanted to the UAV path planning problem and tested under the terrain obstacle models with 6, 9 and 12 peaks. The simulation results show that, compared with other algorithms, AOOA has lower average cost, lower standard deviation, and shorter and more stable path in different terrain scenarios.

    Tools

    Get Citation

    Copy Citation Text

    CEN Zhe, FU Qiang, TONG Nan. UAV Path Planning Based on Adaptive Osprey Optimization Algorithm[J]. Electronics Optics & Control, 2024, 31(11): 26

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category:

    Received: Nov. 2, 2023

    Accepted: Jan. 2, 2025

    Published Online: Jan. 2, 2025

    The Author Email:

    DOI:10.3969/j.issn.1671-637x.2024.11.004

    Topics