This paper presents the second part of the two-part survey series on decomposition-based evolutionary multi-objective optimization where we mainly focus on discussing the literature related to multi-objective evolutionary algorithms based on decomposition (MOEA/D). Complementary to the first part, here we employ a series of advanced data mining approaches to provide a comprehensive anatomy of the enormous landscape of MOEA/D research, which is far beyond the capacity of classic manual literature review protocol. In doing so, we construct a heterogeneous knowledge graph that encapsulates more than 5,400 papers, 10,000 authors, 400 venues, and 1,600 institutions for MOEA/D research. We start our analysis with basic descriptive statistics. Then we delve into prominent research/application topics pertaining to MOEA/D with state-of-the-art topic modeling techniques and interrogate their sptial-temporal and bilateral relationships. We also explored the collaboration and citation networks of MOEA/D, uncovering hidden patterns in the growth of literature as well as collaboration between researchers. Our data mining results here, combined with the expert review in Part I, together offer a holistic view of the MOEA/D research, and demonstrate the potential of an exciting new paradigm for conducting scientific surveys from a data science perspective.

翻译：本文是两篇系列综述的第二部分，聚焦基于分解的多目标进化算法（MOEA/D）相关文献讨论。作为第一部分的补充，我们采用一系列先进数据挖掘方法，对MOEA/D研究领域海量文献进行全方位剖析——这远超传统人工文献综述方法的能力范畴。为此，我们构建了包含5,400余篇论文、10,000余位作者、400余个出版平台和1,600余所研究机构的MOEA/D异构知识图谱。首先通过基础描述性统计展开分析，继而运用前沿主题建模技术深入探讨MOEA/D的主要研究/应用主题，并审视其时空演化与双边关联。我们还剖析了MOEA/D合作网络与引文网络，揭示文献增长规律及研究者合作模式的隐性特征。本文数据挖掘成果与第一部分专家综述相结合，不仅提供了MOEA/D研究的全景视角，更展现出从数据科学视角开展学术综述这一新兴范式的巨大潜力。