Particle filter with swarm move for optimization.

dc.contributor.authorJi, Chunlinen
dc.contributor.authorZhang, Yangyangen
dc.contributor.authorTong, Mengmengen
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2013-06-11T16:00:09Z
dc.date.available2013-06-11T16:00:09Z
dc.date.issued2008
dc.description.abstractWe 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.en
dc.identifier.citationJi, C. et al. (2008) Particle filter with swarm move for optimization. In: Parallel problem solving from nature – PPSN X: Proceedings of the 10th International Conference Dortmund, Germany, September 13-17, 2008. Berlin: Springer-Verlag, pp. 909-918.en
dc.identifier.doihttps://doi.org/10.1007/978-3-540-87700-4_90
dc.identifier.isbn978-3-540-87699-1
dc.identifier.urihttp://hdl.handle.net/2086/8728
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherSpringeren
dc.relation.ispartofseriesLecture notes in computer science;Vol. 5199
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.titleParticle filter with swarm move for optimization.en
dc.typeArticleen

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