  An unbounded external archive (UEA), which stores all nondominated solutions
found during the search process, is frequently used to evaluate the performance
of multi-objective evolutionary algorithms (MOEAs) in recent studies. A recent
benchmarking study also shows that decomposition-based MOEA (MOEA/D) is
competitive with state-of-the-art MOEAs when the UEA is incorporated into
MOEA/D. However, a parameter study of MOEA/D using the UEA has not yet been
performed. Thus, it is unclear how control parameter settings influence the
performance of MOEA/D with the UEA. In this paper, we present an analysis of
control parameters of MOEA/D under two performance evaluation scenarios. One is
a final population scenario where the performance assessment of MOEAs is
performed based on all nondominated solutions in the final population, and the
other is a reduced UEA scenario where it is based on a pre-specified number of
selected nondominated solutions from the UEA. Control parameters of MOEA/D
investigated in this paper include the population size, scalarizing functions,
and the penalty parameter of the penalty-based boundary intersection (PBI)
function. Experimental results indicate that suitable settings of the three
control parameters significantly depend on the choice of an optimization
scenario. We also analyze the reason why the best parameter setting is totally
different for each scenario.
