  A decomposition-based multi-objective evolutionary algorithm with a
differential evolution variation operator (MOEA/D-DE) shows high performance on
challenging multi-objective problems (MOPs). The DE mutation consists of three
key components: a mutation strategy, an index selection method for parent
individuals, and a bound-handling method. However, the configuration of the DE
mutation operator that should be used for MOEA/D-DE has not been thoroughly
investigated in the literature. This configuration choice confuses researchers
and users of MOEA/D-DE. To address this issue, we present a review of the
existing configurations of the DE mutation operator in MOEA/D-DE and
systematically examine the influence of each component on the performance of
MOEA/D-DE. Our review reveals that the configuration of the DE mutation
operator differs depending on the source code of MOEA/D-DE. In our analysis, a
total of 30 configurations (three index selection methods, two mutation
strategies, and five bound handling methods) are investigated on 16 MOPs with
up to five objectives. Results show that each component significantly affects
the performance of MOEA/D-DE. We also present the most suitable configuration
of the DE mutation operator, which maximizes the effectiveness of MOEA/D-DE.
