A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part II -- A Data Science Perspective

• URL: http://arxiv.org/abs/2404.14228v1
• Date: Mon, 22 Apr 2024 14:38:58 GMT
• Title: A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part II -- A Data Science Perspective
• Authors: Mingyu Huang, Ke Li,
• Abstract summary: We build a knowledge graph that encapsulates more than 5,400 papers, 10,000 authors, 400 venues, and 1,600 institutions for MOEA/D research. We also explore the collaboration and citation networks of MOEA/D, uncovering hidden patterns in the growth of literature.
• Score: 4.322038460697958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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