Particle Swarm Convergence: Standardized Analysis and Topological Influence

  • Christopher W. Cleghorn
  • Andries P. Engelbrecht
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8667)


This paper has two primary aims. Firstly, to empirically verify the use of a specially designed objective function for particle swarm optimization (PSO) convergence analysis. Secondly, to investigate the impact of PSO’s social topology on the parameter region needed to ensure convergent particle behavior. At present there exists a large number of theoretical PSO studies, however, all stochastic PSO models contain the stagnation assumption, which implicitly removes the social topology from the model, making this empirical study necessary. It was found that using a specially designed objective function for convergence analysis is both a simple and valid method for convergence analysis. It was also found that the derived region needed to ensure convergent particle behavior remains valid regardless of the selected social topology.


Objective Function Particle Swarm Optimization Particle Swarm Convergence Analysis Ring Topology 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Christopher W. Cleghorn
    • 1
  • Andries P. Engelbrecht
    • 1
  1. 1.Department of Computer ScienceUniversity of PretoriaSouth Africa

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