  Simultaneously visualizing the decision and objective space of continuous
multi-objective optimization problems (MOPs) recently provided key
contributions in understanding the structure of their landscapes. For the sake
of advancing these recent findings, we compiled all state-of-the-art
visualization methods in a single R-package (moPLOT). Moreover, we extended
these techniques to handle three-dimensional decision spaces and propose two
solutions for visualizing the resulting volume of data points. This enables -
for the first time - to illustrate the landscape structures of
three-dimensional MOPs.
  However, creating these visualizations using the aforementioned framework
still lays behind a high barrier of entry for many people as it requires basic
skills in R. To enable any user to create and explore MOP landscapes using
moPLOT, we additionally provide a dashboard that allows to compute the
state-of-the-art visualizations for a wide variety of common benchmark
functions through an interactive (web-based) user interface.
