Research on task allocation of UAV cluster based on particle swarm quantization algorithm
Rongmei Geng, Renxin Ji, Shuanjin Zi
Abstract
For the UAV cluster task allocation problem, the particle swarm optimization algorithm has slow convergence speed, low fitness level, easy to fall into local minimum, and can not obtain the global optimal solution. Aiming at the shortcomings of the traditional particle swarm optimization algorithm, a quantized particle swarm optimization algorithm (named QPSO method) has been designed to adapt to the task allocation problem of UAV cluster in this paper. In this algorithm, the Schrodinger equation is used to construct the quantized particle motion rule, and the Monte Carlo method is used to construct the update mechanism of the quantized particle position. The experimental results show that in the three groups of experiments of reconnaissance, attack and damage, the proposed algorithm has high adaptability, fast convergence speed, reasonable task allocation of UAVs in the cluster, efficient use of UAVs, and the performance of QPSO algorithm is obviously better than the particle swarm optimization algorithm and the genetic algorithm.