J Shanghai Jiaotong Univ Sci ›› 2020, Vol. 25 ›› Issue (6): 681-688.doi: 10.1007/s12204-020-2229-5
• • 下一篇
PENG Pai (彭湃), CHEN Cong (陈聪), YANG Yongsheng (杨永胜)
出版日期:
2020-12-28
发布日期:
2020-11-26
通讯作者:
YANG Yongsheng (杨永胜)
E-mail:ysyang@sjtu.edu.cn
PENG Pai (彭湃), CHEN Cong (陈聪), YANG Yongsheng (杨永胜)
Online:
2020-12-28
Published:
2020-11-26
Contact:
YANG Yongsheng (杨永胜)
E-mail:ysyang@sjtu.edu.cn
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.
中图分类号:
PENG Pai, CHEN Cong , YANG Yongsheng . Particle Swarm Optimization Based on Hybrid Kalman Filter and Particle Filter [J]. J Shanghai Jiaotong Univ Sci, 2020, 25(6): 681-688.
PENG Pai, CHEN Cong , YANG Yongsheng . Particle Swarm Optimization Based on Hybrid Kalman Filter and Particle Filter [J]. J Shanghai Jiaotong Univ Sci, 2020, 25(6): 681-688.
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