  This paper presents a new implementation of deterministic multiobjective (MO)
optimization called Multiobjective Fractal Decomposition Algorithm (Mo-FDA).
The original algorithm was designed for mono-objective large scale continuous
optimization problems. It is based on a divide and conquer strategy and a
geometric fractal decomposition of the search space using hyperspheres. Then,
to deal with MO problems a scalarization approach is used. In this work, a new
approach has been developed on a multi-node environment using containers. The
performance of Mo-FDA was compared to state of the art algorithms from the
literature on classical benchmark of multi-objective optimization
