  This paper is motivated by real-life applications of bi-objective
optimization. Having many non dominated solutions, one wishes to cluster the
Pareto front using Euclidian distances. The p-center problems, both in the
discrete and continuous versions, are proven solvable in polynomial time with a
common dynamic programming algorithm. Having $N$ points to partition in
$K\geqslant 3$ clusters, the complexity is proven in $O(KN\log N)$ (resp
$O(KN\log^2 N)$) time and $O(KN)$ memory space for the continuous (resp
discrete) $K$-center problem. $2$-center problems have complexities in $O(N\log
N)$. To speed-up the algorithm, parallelization issues are discussed. A
posteriori, these results allow an application inside multi-objective
heuristics to archive partial Pareto Fronts.
