J Shanghai Jiaotong Univ Sci ›› 2020, Vol. 25 ›› Issue (6): 681-688.doi: 10.1007/s12204-020-2229-5

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Particle Swarm Optimization Based on Hybrid Kalman Filter and Particle Filter 

Particle Swarm Optimization Based on Hybrid Kalman Filter and Particle Filter 

PENG Pai (彭湃), CHEN Cong (陈聪), YANG Yongsheng (杨永胜)      

  1. (1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China;
    2. Key Laboratory of Integrated Technology of Avionics System, Shanghai 201103, China)
  2. (1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China;
    2. Key Laboratory of Integrated Technology of Avionics System, Shanghai 201103, China)
  • Online:2020-12-28 Published:2020-11-26
  • Contact: YANG Yongsheng (杨永胜) E-mail:ysyang@sjtu.edu.cn

Abstract: The combination of particle swarm and filters is a hot topic in the research of particle swarm optimization (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.


Key words: particle swarm optimization (PSO)| Kalman filter| particle filter| intelligent algorithm


摘要: The combination of particle swarm and filters is a hot topic in the research of particle swarm optimization (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.


关键词: particle swarm optimization (PSO)| Kalman filter| particle filter| intelligent algorithm


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