Particle Swarm Optimization Based on Hybrid Kalman Filter and Particle Filter

• 作 者: PENG Pai CHEN Cong YANG Yongsheng
• 机 构: School of Aeronautics and Astronautics,Shanghai Jiao Tong University,Shanghai 200240,China;Key Labor
• 出 处: 上海交通大学学报（英文版）
• 发表日期: 2020年6期
• 关键词: particle swarm optimization(PSO) Kalman filter particle filter intelligent algor

The combination of particle swarm and filters is a hot topic in the research of particle swarm op-timization(PSO).The Kalman filter based PSO(K-PSO)algorithm is efficient,but it is prone to premature convergence.In this paper,a particle filter based PSO(P-PSO)algorithm is proposed,which is a fine search with fewer premature problems.Unfortunately,the P-PSO algorithm is of higher computational complexity.In order to avoid the premature problem and reduce the computational burden,a hybrid Kalman filter and particle filter based particle swarm optimization(HKP-PSO)algorithm is proposed.The HKP-PSO algorithm combines the fast convergence feature of K-PSO and the consistent convergence performance of P-PSO to avoid premature convergence as well as high computational complexity.The simulation results demonstrate that the proposed HKP-PSO algorithm can achieve better optimal solution than other six PSO related algorithms.