论文标题
MOEA/D-DE中差分进化突变算子的三个组成部分的审查和分析
Review and Analysis of Three Components of Differential Evolution Mutation Operator in MOEA/D-DE
论文作者
论文摘要
具有差异进化变化操作员（MOEA/D-DE）的基于分解的多目标进化算法在具有挑战性的多目标问题（MOPS）上显示出高性能。 DE突变由三个关键组成部分组成：一种突变策略，父母的指数选择方法以及一种界定方法。但是，在文献中尚未彻底研究用于MOEA/D-DE的DE突变算子的配置。这种配置选择使MOEA/D-DE的研究人员和用户感到困惑。为了解决此问题，我们对MOEA/D-DE中DE突变操作员的现有配置进行了综述，并系统地检查了每个组件对MOEA/D-DE性能的影响。我们的评论表明，DE突变操作员的配置取决于MOEA/D-DE的源代码。在我们的分析中，在16个具有多达5个目标的MOP上研究了总共30种配置（三种索引选择方法，两种突变策略和五种绑定的处理方法）。结果表明，每个组件都会显着影响MOEA/D-DE的性能。我们还提出了DE突变操作员的最合适的配置，该配置最大化了MOEA/D-DE的有效性。
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.
