Grouping-Shuffling Particle Swarm Optimization: An Improved PSO for Continuous Optimization

  • Yinghai Li
  • Xiaohua Dong
  • Ji Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)


This paper proposes a novel population-based evolution algorithm named grouping-shuffling particle swarm optimization (GSPSO) by hybridizing particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) for continuous optimization problems. In the proposed algorithm, each particle automatically and periodically executes grouping and shuffling operations in its flight learning evolutionary process. By testing on 4 benchmark functions, the numerical results demonstrate that, the optimization performance of the proposed GSPSO is much better than PSO and SFLA. The GSPSO can both avoid the PSO’s shortage that easy to get rid of the local optimal solution and has faster convergence speed and higher convergence precision than the PSO and SFLA.


Particle swarm optimization Shuffled frog leaping algorithm Evolution strategy Continuous optimization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yinghai Li
    • 1
  • Xiaohua Dong
    • 1
  • Ji Liu
    • 1
  1. 1.College of Hydraulic & Environmental EngineeringChina Three Gorges UniversityYichangChina

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