首页> 外文期刊>Knowledge-Based Systems >A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning
【24h】

A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning

机译:一种新型空中航空公司(UAV)路径规划的新型混合灰狼优化算法

获取原文
获取原文并翻译 | 示例
获取外文期刊封面目录资料

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. (C) 2020 Elsevier B.V. All rights reserved.
机译:无人驾驶飞行器(UAV)路径规划问题是无人机任务规划系统的重要组成部分,需要在复杂的领域获得最佳路线。为了解决这个问题,通过组合简化的灰狼优化器(SGWO)和改性的共生生物搜索(MSO)来提出一种新的混合算法,提出了一种称为HSGWO-MSOS的混合算法。在所提出的算法中,勘探和开发能力有效地合并。简化了GWO算法的阶段以加速收敛速度并保留人口的勘探能力。通过GWO修改和合成SOS算法的共识阶段,以提高利用能力。此外,基于线性差分方程的方法提出了所提出的HSGWO-MSOS算法的收敛性分析。立方B样条曲线用于平滑生成的飞行路线,并使规划路径适合于UAV。仿真实验结果表明,HSGWO-MSOS算法可以成功获取可行和有效的路由,其性能优于GWO,SOS和SA算法。 (c)2020 Elsevier B.v.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号