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Golden eagle optimizer with double learning strategies for 3D path planning of UAV in power inspection

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  • Pan, Jeng-Shyang
  • Lv, Ji-Xiang
  • Yan, Li-Jun
  • Weng, Shao-Wei
  • Chu, Shu-Chuan
  • Xue, Jian-Kai

Abstract

Unmanned aerial vehicle (UAV) inspection is an indispensable part of power inspection. In the process of power inspection, the UAV needs to obtain an efficient and feasible path in the complex environment. To solve this problem, a Golden eagle optimizer with double learning strategies (GEO-DLS) is proposed. The double learning strategies consist of personal example learning and mirror reflection learning. The personal example learning can enhance the search ability of the Golden eagle optimizer (GEO) and reduce the possibility of the GEO falling into the local optimum. The mirror reflection learning can improve the optimization accuracy of the GEO and accelerate the convergence speed of the GEO. To verify the optimization performance of the algorithm, the proposed GEO-DLS and several other algorithms were tested under the CEC2013 test suite. At the same time, the proposed GEO-DLS and the GEO were analyzed for population diversity and exploration–exploitation​ ratio. Finally, the proposed GEO-DLS is applied to the UAV path planning to generate the initial path, and the cubic B-spline curve is used to smooth the path. These experimental results show that the GEO-DLS has a good performance. The code can be publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/98799-golden-eagle-optimizer-with-double-learning-strategies

Suggested Citation

  • Pan, Jeng-Shyang & Lv, Ji-Xiang & Yan, Li-Jun & Weng, Shao-Wei & Chu, Shu-Chuan & Xue, Jian-Kai, 2022. "Golden eagle optimizer with double learning strategies for 3D path planning of UAV in power inspection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 509-532.
  • Handle: RePEc:eee:matcom:v:193:y:2022:i:c:p:509-532
    DOI: 10.1016/j.matcom.2021.10.032
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    References listed on IDEAS

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    1. Pan, Jeng-Shyang & Hu, Pei & Chu, Shu-Chuan, 2021. "Binary fish migration optimization for solving unit commitment," Energy, Elsevier, vol. 226(C).
    2. Yong-bo Chen & Guan-chen Luo & Yue-song Mei & Jian-qiao Yu & Xiao-long Su, 2016. "UAV path planning using artificial potential field method updated by optimal control theory," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(6), pages 1407-1420, April.
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    1. Tsu-Yang Wu & Qian Meng & Yeh-Cheng Chen & Saru Kumari & Chien-Ming Chen, 2023. "Toward a Secure Smart-Home IoT Access Control Scheme Based on Home Registration Approach," Mathematics, MDPI, vol. 11(9), pages 1-15, April.

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