Particle Swarm Optimization Based on Hybrid Kalman Filter and Particle Filter

摘要/Abstract

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

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

中图分类号:

TP 183

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.

参考文献23

[1]	KENNEDY J. Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance[C]//Proceedings of the 1999 Congress on Evolutionary Computation-CEC99. Washington, DC,USA: IEEE, 1999: 1931-1938.
[2]	KENNEDY J, EBERHART R. Particle swarm optimization[C]//IEEE International Conference on Neural Networks. Perth, Australia: IEEE, 1995: 1942-1948.
[3]	CHAUHAN P, DEEP K, PANT M. Novel inertia weight strategies for particle swarm optimization [J].Memetic Computing, 2013, 5(3): 229-251.
[4]	MENDES R, KENNEDY J, NEVES J. Watch the neighbor or how the swarm can learn from its environment[C]//Proceedings of the 2003 IEEE Swarm Intelligence Symposium. Indianapolis, IN, USA: IEEE,2003: 88-94.
[5]	MENDES R, KENNEDY J, NEVES J. The fully informed particle swarm: Simpler, maybe better[J]. IEEE Transactions on Evolutionary Computation,2004, 8(3): 204-210.
[6]	KENNEDY J, MENDES R. Neighborhood topologies in fully-informed and best-of-neighborhood particle swarms [J]. IEEE Transactions on Systems, Man,and Cybernetics, Part C (Applications and Reviews),2006, 36(4): 515-519.
[7]	SHI Y H, EBERHART R. A modified particle swarm optimizer [C]//Proceedings of the 1998 IEEE International Conference On Evolutionary Computation. Anchorage,AK, USA: IEEE, 1998: 69-73.
[8]	SHI Y H, EBERHART R C. Fuzzy adaptive particle swarm optimization [C]//Proceedings of the 2001 Congress on Evolutionary Computation. Seoul, South Korea: IEEE, 2001: 101-106.
[9]	CLERCM, KENNEDYJ. The particle swarm: Explosion,stability, and convergence in a multidimensional complex space [J]. IEEE Transactions on Evolutionary Computation, 2002, 6(1): 58-73.
[10]	RATNAWEERA A, HALGAMUGE S K, WATSON H C. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients [J].IEEE Transactions on Evolutionary Computation,2004, 8(3): 240-255.
[11]	ZHAN Z H, ZHANG J, LI Y, et al. Adaptive particle swarm optimization [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics),2009, 39(6): 1362-1381.
[12]	NICKABADI A, EBADZADEHMM, SAFABAKHSH R. A novel particle swarm optimization algorithm with adaptive inertia weight [J]. Applied Soft Computing,2011, 11(4): 3658-3670.
[13]	ZAHARIS Z D, GRAVAS I P, YIOULTSIS T V, et al. Exponential log-periodic antenna design using improved particle swarm optimization with velocity mutation[J]. IEEE Transactions on Magnetics, 2017,53(6): 7204104.
[14]	YAN X H, HE F Z, HOU N, et al. An efficient particle swarm optimization for large-scale hardware/software co-design system [J]. International Journal of Cooperative Information Systems, 2018, 27(1): 1741001.
[15]	LI C H, YANG S X, NGUYEN T T. A self-learning particle swarm optimizer for global optimization problems[J]. IEEE Transactions on Systems, Man, and Cybernetics,Part B (Cybernetics), 2012, 42(3): 627-646.
[16]	TANWEER M R, SURESH S, SUNDARARAJAN N.Self regulating particle swarm optimization algorithm[J]. Information Sciences, 2015, 294: 182-202.
[17]	MONSON C K, SEPPI K D. The Kalman swarm:A new approach to particle motion in swarm optimization[J]. Lecture Notes in Computer Science, 2004,3102: 140-150.
[18]	XU Y F, CHEN G C, YU J S. The Kalman particle swarm optimization algorithm and its application in soft-sensor of acrylonitrile yield [M]//Advances in natural computation. Berlin, Germany: Springer, 2006:176-179.
[19]	M¨AKIL¨A P M, PAATTILAMMI J. Steady-state Kalman-filtering with nonstationary noise [J]. Optimal Control Applications and Methods, 2003, 24(2): 57-71.
[20]	WU Y L, LIU G, GUO X P, et al. A self-adaptive chaos and Kalman filter-based particle swarm optimization for economic dispatch problem [J]. Soft Computing,2017, 21: 3353-3365.
[21]	NUMMIARO K, KOLLER-MEIER E, VAN GOOL L.An adaptive color-based particle filter [J]. Image and Vision Computing, 2003, 21(1): 99-110.
[22]	PEI F J, CUI P Y, CHEN Y Z. AdaptiveMCMC particle filter for nonlinear and non-Gaussian state estimation[C]//Proceedings of the 3rd International Conference on Innovative Computing Information and Control.Dalian, China: IEEE, 2008: 494-494.
[23]	DOUCET A, FREITAS N D, GORDON N. Sequential Monte Carlo methods in practice [M]. New York, USA:Springer-Verlag, 2001: 106.