  In the evolutionary multi-objective optimization (EMO) field, the standard
practice is to present the final population of an EMO algorithm as the output.
However, it has been shown that the final population often includes solutions
which are dominated by other solutions generated and discarded in previous
generations. Recently, a new EMO framework has been proposed to solve this
issue by storing all the non-dominated solutions generated during the evolution
in an archive and selecting a subset of solutions from the archive as the
output. The key component in this framework is the subset selection from the
archive which usually stores a large number of candidate solutions. However,
most studies on subset selection focus on small candidate solution sets for
environmental selection. There is no benchmark test suite for large-scale
subset selection. This paper aims to fill this research gap by proposing a
benchmark test suite for subset selection from large candidate solution sets,
and comparing some representative methods using the proposed test suite. The
proposed test suite together with the benchmarking studies provides a baseline
for researchers to understand, use, compare, and develop subset selection
methods in the EMO field.
