Particle Swarm Optimization with Spatially Meaningful Neighbours
Neighbourhood topologies in particle swarm optimization (PSO) are typically random in terms of the spatial positions of connected neighbours. This study explores the use of spatially meaningful neighbours for PSO. An approach is designed which uses heuristics to leverage the natural neighbours computed with Delaunay triangulation. The approach is compared to standard PSO sociometries and fitness distance ratio approaches. Although intrinsic properties of Delaunay triangulation limit the practical application of this approach to low dimensions results show that it is a successful particle swarm optimizer.