摘要
提出了一种应用于无人机三维航迹规划的改进智能单粒子优化(ISPO)算法。把ISPO算法应用于航迹规划,并在此基础上引入子矢量之间的吸引效应,有效克服了算法易陷入局部最优解的缺陷。通过仿真实验,分别把改进ISPO算法、带动态惯性权系数的粒子群优化(PSO)算法及具备反向学习和局部学习的粒子群(RLPSO)算法应用于航迹规划。仿真结果表明,改进ISPO算法在航迹规划问题上有更强的寻优精度和能力,效率也高于另外两种算法。
An improved Intelligent Single Particle Optimizer（ ISPO） algorithm is proposed for 3 D route planning of Unmanned Aerial Vehicles（ UAVs）. The ISPO algorithm is applied to the route planning,and the attracting effect between sub-vectors is introduced to the improved algorithm,which effectively overcomes the shortcoming that the algorithm is easy to fall into the locally optimal solution. Simulation experiments are carried out,in which the improved ISPO algorithm,the Particle Swarm Optimizer（ PSO） algorithm with dynamic inertia coefficient and the PSO with reverse-learning and local-learning capabilities（ RLPSO） are applied to the route planning. The simulation results show that the improved ISPO algorithm has the best searching precision and ability in the route planning,and its efficiency is higher than that of the other two algorithms.
作者
刘志阳
江涛
甄云卉
LIU Zhi-yang;JIANG Tao;ZHEN Yun-hui(Shijiazhuang Campus, The Army Engineering University, Shijiazhuang 050003, China;Hebei Military Area Command, Shijiazhuang 050003, China)
出处
《电光与控制》
北大核心
2018年第7期48-53,共6页
Electronics Optics & Control
关键词
无人机
航迹规划
改进IPSO算法
吸引效应
unmanned aerial vehicle
route planning
improved ISPO algorithm
attracting effect
