  In this paper, we conduct a fitness landscape analysis for multiobjective
combinatorial optimization, based on the local optima of multiobjective
NK-landscapes with objective correlation. In single-objective optimization, it
has become clear that local optima have a strong impact on the performance of
metaheuristics. Here, we propose an extension to the multiobjective case, based
on the Pareto dominance. We study the co-influence of the problem dimension,
the degree of non-linearity, the number of objectives and the correlation
degree between objective functions on the number of Pareto local optima.
