  Visualisation is an effective way to facilitate the analysis and
understanding of multivariate data. In the context of multi-objective
optimisation, comparing to quantitative performance metrics, visualisation is,
in principle, able to provide a decision maker better insights about Pareto
front approximation sets (e.g. the distribution of solutions, the geometric
characteristics of Pareto front approximation) thus to facilitate the
decision-making (e.g. the exploration of trade-off relationship, the knee
region or region of interest). In this paper, we overview some currently
prevalent visualisation techniques according to the way how data is
represented. To have a better understanding of the pros and cons of different
visualisation techniques, we empirically compare six representative
visualisation techniques for the exploratory analysis of different Pareto front
approximation sets obtained by four state-of-the-art evolutionary
multi-objective optimisation algorithms on the classic DTLZ benchmark test
problems. From the empirical results, we find that visual comparisons also
follow the \textit{No-Free-Lunch} theorem where no single visualisation
technique is able to provide a comprehensive understanding of the
characteristics of a Pareto front approximation set. In other words, a specific
type of visualisation technique is only good at exploring a particular aspect
of the data.
