  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.
