  The paper analyzes the scalability of multiobjective estimation of
distribution algorithms (MOEDAs) on a class of boundedly-difficult
additively-separable multiobjective optimization problems. The paper
illustrates that even if the linkage is correctly identified, massive
multimodality of the search problems can easily overwhelm the nicher and lead
to exponential scale-up. Facetwise models are subsequently used to propose a
growth rate of the number of differing substructures between the two objectives
to avoid the niching method from being overwhelmed and lead to polynomial
scalability of MOEDAs.
