标题:A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning
作者:Qu C.; Gai W.; Zhang J.; Zhong M.
作者机构:[Qu, C] Shandong University of Science and Technology, Qingdao, 266590, China;[ Gai, W] Shandong University of Science and Technology, Qingdao, 266590 更多
通讯作者:Gai, W(gwd2011@sdust.edu.cn)
通讯作者地址:[Gai, W] Shandong University of Science and TechnologyChina;
来源:Knowledge-Based Systems
出版年:2020
DOI:10.1016/j.knosys.2020.105530
关键词:Hybrid meta-heuristic algorithm; Path planning; Unmanned aerial vehicle (UAV)
摘要:Unmanned aerial vehicle (UAV) path planning problem is an important component of UAV mission planning system, which needs to obtain optimal route in the complicated field. To solve this problem, a novel hybrid algorithm called HSGWO-MSOS is proposed by combining simplified grey wolf optimizer (SGWO) and modified symbiotic organisms search (MSOS). In the proposed algorithm, the exploration and exploitation abilities are combined efficiently. The phase of the GWO algorithm is simplified to accelerate the convergence rate and retain the exploration ability of the population. The commensalism phase of the SOS algorithm is modified and synthesized with the GWO to improve the exploitation ability. In addition, the convergence analysis of the proposed HSGWO-MSOS algorithm is presented based on the method of linear difference equation. The cubic B-spline curve is used to smooth the generated flight route and make the planning path be suitable for the UAV. The simulation experimental results show that the HSGWO-MSOS algorithm can acquire a feasible and effective route successfully, and its performance is superior to the GWO, SOS and SA algorithm. © 2020 Elsevier B.V.
收录类别:SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078795296&doi=10.1016%2fj.knosys.2020.105530&partnerID=40&md5=53c08da003fead3e92e66c82193ba02e
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