Scalability Study of Particle Swarm Optimizers in Dynamic Environments

  • Barend J. Leonard
  • Andries P. Engelbrecht
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7461)


This study investigates the scalability of three particle swarm optimizers (PSO) on dynamic environments. The charged PSO (CPSO), quantum PSO (QPSO) and dynamic heterogeneous PSO (dHPSO) algorithms are evaluated on a number of DF1 and moving peaks benchmark (MPB) environments that differ with respect to the severity and frequency of change. It is shown that dHPSO scales better to high severity and high frequency DF1 environments. For MPB environments, similar scalability results are observed, with dHPSO obtaining the best average results over all test cases. The good performance of dHPSO is ascribed to its ability to explore and exploit the search space more efficiently than CPSO and QPSO.


Particle Swarm Optimization Particle Swarm Dynamic Environment Particle Swarm Optimization Algorithm Neutral Particle 
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 2012

Authors and Affiliations

  • Barend J. Leonard
    • 1
  • Andries P. Engelbrecht
    • 1
  1. 1.Department of Computer ScienceUniversity of PretoriaSouth Africa

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