  In this paper, p-dispersion problems are studied to select $p\geqslant 2$
representative points from a large 2D Pareto Front (PF), solution of
bi-objective optimization. Four standard p-dispersion variants are considered.
A novel variant, Max-Sum-Neighbor p-dispersion, is introduced for the specific
case of a 2D PF. Firstly, $2$-dispersion and $3$-dispersion problems are proven
solvable in $O(n)$ time in a 2D PF. Secondly, dynamic programming algorithms
are designed for three p-dispersion variants, proving polynomial complexities
in a 2D PF. Max-min p-dispersion is solvable in $O(pn\log n)$ time and $O(n)$
memory space. Max-Sum-Neighbor p-dispersion is proven solvable in $O(pn^2)$
time and{$O(n)$} space. Max-Sum-min p-dispersion is solvable in $O(pn^3)$ time
and $O(pn^2)$ space, this complexity holds also in 1D, proving for the first
time that Max-Sum-min p-dispersion is polynomial in 1D. Furthermore, properties
of these algorithms are discussed for an efficient implementation {and for a
practical application inside bi-objective meta-heuristics.
