计算机科学
鹰
路径(计算)
运动规划
人口
趋同(经济学)
深度学习
反射(计算机编程)
人工智能
算法
数学优化
模拟
数学
机器人
古生物学
人口学
社会学
经济
生物
程序设计语言
经济增长
作者
Jeng‐Shyang Pan,Jixiang Lv,Lijun Yan,Shaowei Weng,Shu-Chuan Chu,Jiankai Xue
标识
DOI:10.1016/j.matcom.2021.10.032
摘要
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
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