  Scalarization allows to solve a multi-objective optimization problem by
solving many single-objective sub-problems, uniquely determined by some
parameters. In this work, we propose several adaptive strategies to select such
parameters in order to obtain a uniform approximation of the Pareto front. This
is done by introducing a heuristic dynamics where the parameters interact
through a binary repulsive potential. The approach aims to minimize the
associated energy potential which is used to quantify the diversity of the
computed solutions. A stochastic component is also added to overcome
non-optimal energy configurations. Numerical experiments show the validity of
the proposed approach for bi- and tri-objectives problems with different Pareto
front geometries.
