Application of simulated annealing particle swarm optimization in complex three-dimensional path planning

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 algor...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-04, Vol.1873 (1), p.12077
Main Authors: Wangsheng, Fang, Chong, Wang, Ruhua, Zhao
Format: Article
Language:English
Subjects:
Algorithms
Dimensional stability
Machine learning
Particle swarm optimization
Path planning
Physics
Simulated annealing
Simulation
Unmanned aerial vehicles
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Summary: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.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1873/1/012077