Controlling the Parameters of the Particle Swarm Optimization with a Self-Organized Criticality Model

  • Carlos M. Fernandes
  • Juan J. Merelo
  • Agostinho C. Rosa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7492)


This paper investigates a Particle Swarm Optimization (PSO) with a Self-Organized Criticality (SOC) strategy that controls the parameter values and perturbs the position of the particles. The algorithm uses a SOC system known as Bak-Sneppen for establishing the inertia weight and acceleration coefficients for each particle in each time-step. Besides adjusting the parameters, the SOC model may be also used to perturb the particles’ positions, thus increasing exploration and preventing premature convergence. The implementation of both schemes is straightforward and does not require hand-tuning. An empirical study compares the Bak-Sneppen PSO (BS-PSO) with other PSOs, including a state-of-the-art algorithm with dynamic variation of the weight and perturbation of the particles. The results demonstrate the validity of the algorithm.


Particle Swarm Optimization Inertia Weight Ring Topology Dynamic Optimization Problem Acceleration Coefficient 
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

  • Carlos M. Fernandes
    • 1
    • 2
  • Juan J. Merelo
    • 2
  • Agostinho C. Rosa
    • 1
  1. 1.Technical University of LisbonPortugal
  2. 2.University of GranadaSpain

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