Review and Analysis of Three Components of Differential Evolution Mutation Operator in MOEA/D-DE
This work addresses a configuration issue that confuses researchers and users of MOEA/D-DE, providing incremental guidance for optimizing this algorithm in multi-objective evolutionary computing.
This paper tackles the lack of thorough investigation into configuring the differential evolution mutation operator in MOEA/D-DE by reviewing existing configurations and analyzing the influence of its three components on performance across 16 multi-objective problems with up to five objectives, finding that each component significantly affects performance and presenting the most suitable configuration to maximize effectiveness.
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.