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

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7492)

Abstract

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.

Keywords

  • 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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Fernandes, C.M., Merelo, J.J., Rosa, A.C. (2012). Controlling the Parameters of the Particle Swarm Optimization with a Self-Organized Criticality Model. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_16

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  • DOI: https://doi.org/10.1007/978-3-642-32964-7_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32963-0

  • Online ISBN: 978-3-642-32964-7

  • eBook Packages: Computer ScienceComputer Science (R0)