Many modern multiobjective evolutionary algorithms (MOEAs) store the
points discovered during optimization in an external archive,
separate from the main population, as a source of innovation and/or
for presentation at the end of a run. Maintaining a bound on the size
of the archive may be desirable or necessary for several reasons, but
choosing  which points to discard and which to keep in the archive, as
they are discovered, is not trivial. In this paper we briefly review
the state-of-the-art in bounded archiving, and present a new method
based on locally maximizing the hypervolume dominated by the
archive. The new archiver is shown to outperform existing methods,
on several problem instances, with respect to quality of the final
archive obtained when judged using three distinct quality measures.