Open AccessApplication of simulated annealing particle swarm optimization in complex three-dimensional path planning
Author(s) -
Wangsheng Fang,
Chong Wang,
Zhao Ruhua
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1873/1/012077
Subject(s) - simulated annealing , particle swarm optimization , mathematical optimization , motion planning , path (computing) , computer science , swarm behaviour , multi swarm optimization , local optimum , population , algorithm , mathematics , artificial intelligence , demography , sociology , robot , programming language
Particle Swarm Optimization (PSO) has achieved good results in UAV path planning, but there is still the phenomenon of abandoning the global optimal path and choosing the local optimal one. In order to improve the ability of particle swarm in path planning, a simulated annealing particle swarm algorithm is proposed. First, tent reverse learning is used to initialize the population so that the algorithm is evenly distributed in space. Then annealing operation is performed after iteration once, which has better local path judgment ability and avoids the phenomenon of local optimum to some extent, so as to find a more satisfactory path. Simulated annealing particle swarms can find a clear and satisfactory path with high stability through the complex three-dimensional path planning simulation of UAV.