Review and Analysis of Three Components of Differential Evolution Mutation Operator in MOEA/D-DE
Ryoji Tanabe, Hisao Ishibuchi

TL;DR
This paper systematically reviews and analyzes the impact of three key components of the differential evolution mutation operator in MOEA/D-DE, revealing their influence on performance and identifying optimal configurations for multi-objective optimization.
Contribution
It provides the first comprehensive analysis of DE mutation components in MOEA/D-DE, offering guidance on configuration choices to enhance algorithm performance.
Findings
Each component significantly affects MOEA/D-DE performance.
Different configurations yield varying effectiveness across problems.
The study identifies the most suitable configuration for optimal results.
Abstract
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…
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