  The distribution of objective vectors in a Pareto Front Approximation (PFA)
is crucial for representing the associated manifold accurately. Distribution
Indicators (DIs) assess the distribution of a PFA numerically, utilizing
concepts like distance calculation, Biodiversity, Entropy, Potential Energy, or
Clustering. Despite the diversity of DIs, their strengths and weaknesses across
assessment scenarios are not well-understood. This paper introduces a taxonomy
for classifying DIs, followed by a preference analysis of nine DIs, each
representing a category in the taxonomy. Experimental results, considering
various PFAs under controlled scenarios (loss of coverage, loss of uniformity,
pathological distributions), reveal that some DIs can be misleading and need
cautious use. Additionally, DIs based on Biodiversity and Potential Energy show
promise for PFA evaluation and comparison of Multi-Objective Evolutionary
Algorithms.
