  Recent studies on resource allocation suggest that some subproblems are more
important than others in the context of the MOEA/D, and that focusing on the
most relevant ones can consistently improve the performance of that algorithm.
These studies share the common characteristic of updating only a fraction of
the population at any given iteration of the algorithm. In this work we
investigate a new, simpler partial update strategy, in which a random subset of
solutions is selected at every iteration. The performance of the MOEA/D using
this new resource allocation approach is compared experimentally against that
of the standard MOEA/D-DE and the MOEA/D with relative improvement-based
resource allocation. The results indicate that using the MOEA/D with this new
partial update strategy results in improved HV and IGD values, and a much
higher proportion of non-dominated solutions, particularly as the number of
updated solutions at every iteration is reduced.
