What hides in dimension X? A quest for visualizing particle swarms

Abstract

The way we perform evolutionary experiments is all influenced by visualizing multi-dimensional solutions, analyzing the extent to which the search space is explored, displaying the gross population statistics, determining clustering and building blocks, and finding successful combinations of parameter values. Through visualization we can gain valuable insights to enhance our knowledge about particle swarm optimizers, in particular, and the search space that is being explored. In this paper, we focus on different visualization techniques for particle swarm systems. We investigate the advantages of a range of graphical data representation methods by example of the two- and four-dimensional sphere function, the two-dimensional simplified foxholes function, and a 56-dimensional real-world example in the context of muscle stimulus patterns. © 2008 Springer-Verlag Berlin Heidelberg.