  The projection pursuit (PP) guided tour interactively optimises a criteria
function known as the PP index, to explore high-dimensional data by revealing
interesting projections. The optimisation in PP can be non-trivial, involving
non-smooth functions and optima with a small squint angle, detectable only from
close proximity. To address these challenges, this study investigates the
performance of a recently introduced swarm-based algorithm, Jellyfish Search
Optimiser (JSO), for optimising PP indexes. The performance of JSO for
visualising data is evaluated across various hyper-parameter settings and
compared with existing optimisers. Additionally, this work proposes novel
methods to quantify two properties of the PP index, smoothness and
squintability that capture the complexities inherent in PP optimisation
problems. These two metrics are evaluated along with JSO hyper-parameters to
determine their effects on JSO success rate. Our numerical results confirm the
positive impact of these metrics on the JSO success rate, with squintability
being the most significant. The JSO algorithm has been implemented in the tourr
package and functions to calculate smoothness and squintability are available
in the ferrn package.
