  When and why can evolutionary multi-objective optimization (EMO) algorithms
cover the entire Pareto set? That is a major concern for EMO researchers and
practitioners. A recent theoretical study revealed that (roughly speaking) if
the Pareto set forms a topological simplex (a curved line, a curved triangle, a
curved tetrahedron, etc.), then decomposition-based EMO algorithms can cover
the entire Pareto set. Usually, we cannot know the true Pareto set and have to
estimate its topology by using the population of EMO algorithms during or after
the runtime. This paper presents a data-driven approach to analyze the topology
of the Pareto set. We give a theory of how to recognize the topology of the
Pareto set from data and implement an algorithm to judge whether the true
Pareto set may form a topological simplex or not. Numerical experiments show
that the proposed method correctly recognizes the topology of high-dimensional
Pareto sets within reasonable population size.
