J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2020 , Vol. 25 >Issue 6: 681 - 688

DOI: https://doi.org/10.1007/s12204-020-2229-5

Particle Swarm Optimization Based on Hybrid Kalman Filter and Particle Filter 

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  • (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 published: 2020-11-26

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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

Cite this article

PENG Pai, CHEN Cong , YANG Yongsheng . Particle Swarm Optimization Based on Hybrid Kalman Filter and Particle Filter [J]. Journal of Shanghai Jiaotong University(Science), 2020 , 25(6) : 681 -688 . DOI: 10.1007/s12204-020-2229-5

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