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A new hybrid algorithm based on golden eagle optimizer and grey wolf optimizer for 3D path planning of multiple UAVs in power inspection

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Abstract

As an emerging power inspection method, unmanned aerial vehicle (UAV) inspection has the advantages of high safety, high efficiency, and low cost. In the process of power inspection, UAVs need to inspect multiple task points in a complex environment and plan an efficient and feasible path. In this research, the multiple UAVs inspection in the two cases of initial task points and newly added task points is considered. Aiming at these two cases, a hybrid algorithm is proposed in this paper. Firstly, the personal example learning strategy is applied to the golden eagle optimizer (GEO) to get a personal example learning GEO called PELGEO to improve the search ability of the GEO and reduce the possibility of GEO falling into a local optimum. Secondly, the grey wolf optimizer (GWO) is simplified and the differential mutation strategy is introduced to create the simplified GWO with differential mutation called DMSGWO. Finally, to give full play to the advantages of the PELGEO and the DMSGWO, an adaptive hybridization strategy is used to hybridize PELGEO and DMSGWO. The new hybrid algorithm based on GEO and GWO named HGEOGWO is proposed. The HGEOGWO and other algorithms are tested under the CEC2013 test suite. The experimental results show that the HGEOGWO has better optimization performance and stability than some popular algorithms. For the 3D path planning problem of multiple UAVs in power inspection, the proposed algorithm also has obvious advantages compared with some popular algorithms. The code of HGEOGWO can be publicly available at https://www.mathworks.com/matlabcentral/fileexchange/97807-a-new-hybrid-algorithm-based-on-geo-and-gwo.

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Authors and Affiliations

  1. School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou,, 350118, China

    Ji-Xiang Lv, Jeng-Shyang Pan & Xian-Kang He

  2. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Shu-Chuan Chu & Jeng-Shyang Pan

  3. Department of Information Management, Chaoyang University of Technology, Taichung, 413, Taiwan

    Jeng-Shyang Pan

  4. School of Computer Science, Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Li-Jun Yan

  5. College of Science and Engineering, Flinders University, 1284 South Road, Clovelly Park, Adelaide, SA, 5042, Australia

    Shu-Chuan Chu

  6. School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, 350118, China

    Zhi-Ming Cai

  7. College of Information Science and Technology, Donghua University, Shanghai, 201620, China

    Jian-Kai Xue

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  1. Ji-Xiang Lv
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Lv, JX., Yan, LJ., Chu, SC. et al. A new hybrid algorithm based on golden eagle optimizer and grey wolf optimizer for 3D path planning of multiple UAVs in power inspection. Neural Comput & Applic 34, 11911–11936 (2022). https://doi.org/10.1007/s00521-022-07080-0

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