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Using Fluid Neural Networks to Create Dynamic Neighborhood Topologies in Particle Swarm Optimization

  • Stephen M. Majercik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8667)

Abstract

Fluid Neural Networks (FNNs) are a model of interacting mobile automata. The automata move on a lattice, affecting each other’s motion in a way that can result in clusters of automata that change over time, making FNNs a potential basis for dynamic neighborhood topologies in Particle Swarm Optimization. We describe Fluid Neural Network Particle Swarm Optimization (FNN-PSO), a PSO algorithm that uses a dynamic neighborhood mechanism based on FNNs, and we report promising results from experiments indicating that FNN-PSO can outperform both the standard PSO algorithm and PCGT-PSO, a PSO algorithm based on partially connected grid topologies[3], over a range of neighborhood topologies and influence models.

Keywords

Particle Swarm Optimization Particle Swarm Optimization Algorithm Standard Particle Swarm Optimization Particle Swarm Optimization Variant Dynamic Neighborhood 
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

  • Stephen M. Majercik
    • 1
  1. 1.Computer Science DepartmentBowdoin CollegeBrunswickUSA

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