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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/5977

Title: Particle filter with swarm move for optimization
Authors: Ji, C
Zhang, Y
Tong, M
Yang, S
Keywords: Particle filter
Swarm move method
Particle swarm optimization
Optimization problem
Publication Date: 2008
Publisher: Springer
Citation: 10th International Conference on Parallel Problem Solving from Nature (PPSN X), Lectures in Computer Science 5199: 909 - 918, 2008
Abstract: We propose a novel generalized algorithmic framework to utilize particle filter for optimization incorporated with the swarm move method in particle swarm optimization (PSO). In this way, the PSO update equation is treated as the system dynamic in the state space model, while the objective function in optimization problem is designed as the observation/measurement in the state space model. Particle filter method is then applied to track the dynamic movement of the particle swarm and therefore results in a novel stochastic optimization tool, where the ability of PSO in searching the optimal position can be embedded into the particle filter optimization method. Finally, simulation results show that the proposed novel approach has significant improvement in both convergence speed and final fitness in comparison with the PSO algorithm over a set of standard benchmark problems.
Description: Copyright @ Springer-Verlag Berlin Heidelberg 2008.
Sponsorship: The work was supported by the Engineering and Physical Sciences Research Council(EPSRC) of UK under Grant EP/E060722/1.
URI: http://www.springerlink.com/content/7388t8446n821099/
http://bura.brunel.ac.uk/handle/2438/5977
DOI: http://dx.doi.org/10.1007/978-3-540-87700-4_90
ISSN: 0302-9743
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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