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Particle Filter with Swarm Move for Optimization

  • Chunlin Ji
  • Yangyang Zhang
  • Mengmeng Tong
  • Shengxiang Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

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.

Keywords

Particle Swarm Optimization Particle Filter Particle Swarm Optimization Algorithm State Space Model Importance Sampling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Chunlin Ji
    • 1
  • Yangyang Zhang
    • 2
  • Mengmeng Tong
    • 3
  • Shengxiang Yang
    • 4
  1. 1.Department of Statistical SciencesDuke UniversityDurhamUSA
  2. 2.Department of Engineering ScienceUniversity of OxfordOxfordUK
  3. 3.School of Information Science and TechnologyNortheastern UniversityShenyangChina
  4. 4.Department of Computer ScienceUniversity of LeicesterLE1 7RHUK

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