A simulated annealing-particle swarm optimization algorithm for UAV multi-target path planning

In the application of data collection of the Internet of Things by unmanned aerial vehicle (UAV), UAV multi-target path planning (MPP) has an important impact. Aiming at the disadvantage of classic particle swarm optimization algorithm that is easy to fall into local optimum when solving the problem of UAV-MPP, this paper introduces the annealing mechanism and Metropolis criterion to update the particle state through simulated annealing probability jump strategy. The simulated annealing-particle swarm optimization (SA-PSO) algorithm reduces the probability of particles falling into a local optimum and improves the performance of the particle swarm algorithm in dealing with the UAV-MPP problem. The simulation results indicate that the proposed SA-PSO algorithm has the characteristics of strong ability to avoid local optima and the faster convergence speed.