Research on Target Localization Based on Improved Adaptive Velocity Particle Swarm Optimization Algorithm
基于改进自适应粒子群算法的目标定位方法

An improved adaptive particle swarm optimization algorithm based on velocity adaption and mutation adaption was proposed in view of the shortcoming of the existing localization algorithm and standard particle swarm optimizer algorithm, which has complex calculation, convergence speed and large computational load. The method has selected the particle swarm on the adaptive velocity particle swarm optimization algorithm, added adaptive mutation operation in iteration process to enhance its ability of quick convergence, and the mutation probability is adaptively adjusted by variance of the population' s fitness. The simulation results indicate that it could carry on the localization effectively through adopting the improved adaptive particle swarm optimization algorithm. when the variance of random noise interference is 0. 5, the localization RMSE is below 1. 5m, and has high convergence speed and low computational load.