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.
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Majercik, S.M. (2014). Using Fluid Neural Networks to Create Dynamic Neighborhood Topologies in Particle Swarm Optimization. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2014. Lecture Notes in Computer Science, vol 8667. Springer, Cham. https://doi.org/10.1007/978-3-319-09952-1_26
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DOI: https://doi.org/10.1007/978-3-319-09952-1_26
Publisher Name: Springer, Cham
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