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A decomposition-based multi-objective evolutionary algorithm with adifferential evolution variation operator (MOEA/D-DE) shows high performance onchallenging multi-objective problems (MOPs). The DE mutation consists of threekey components: a mutation strategy, an index selection method for parentindividuals, and a bound-handling method. However, the configuration of the DEmutation operator that should be used for MOEA/D-DE has not been thoroughlyinvestigated in the literature. This configuration choice confuses researchersand users of MOEA/D-DE. To address this issue, we present a review of theexisting configurations of the DE mutation operator in MOEA/D-DE andsystematically examine the influence of each component on the performance ofMOEA/D-DE. Our review reveals that the configuration of the DE mutationoperator differs depending on the source code of MOEA/D-DE. In our analysis, atotal of 30 configurations (three index selection methods, two mutationstrategies, and five bound handling methods) are investigated on 16 MOPs withup to five objectives. Results show that each component significantly affectsthe performance of MOEA/D-DE. We also present the most suitable configurationof the DE mutation operator, which maximizes the effectiveness of MOEA/D-DE.

Review and Analysis of Three Components of Differential Evolution Mutation Operator in MOEA/D-DE