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.
Aiming at the defects of fast convergence speed of lion swarm algorithm and easy to fall into local optimum, a variable-speed elastic collision lion swarm algorithm (VELSO) is proposed. Firstly, the flexibility of lioness search is increased according to the variable spiral search strategy, and then the two lionesses learn the learning strategies of teaching and learning algorithms to enhance the interactive behavior of lioness hunting. Then, the improved refraction reverse learning strategy is used to increase the diversity of the population and make the individual quality of the population better. Finally, the variablespeed elastic collision strategy is used to increase the probability of the algorithm jumping out of the local optimum and improve the ability of the algorithm to obtain the optimal solution. In order to verify the effectiveness of the proposed algorithm, 16 test functions were used to test the proposed algorithm, and compared with other algorithms, which proved that the proposed algorithm is very effective. Finally, the proposed algorithm is applied to DV-Hop positioning, which verifies the feasibility and practicability of the proposed algorithm.
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