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)


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.


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