Pareto dominance (PD) has been the most commonly adopted relation to compare solutions in the multiobjective optimization context. Multiobjective evolutionary algorithms (MOEAs) based on PD have been successfully used in order to optimize bi-objective and three-objective problems. However, it has been shown that Pareto dominance loses its effectiveness as the number of objectives increases and thus, the convergence behavior of approaches based on this concept decreases. This paper tackles the MOEAs’ scalability problem that arises as we increase the number of objective functions. In this paper, we perform a comparative study of some of the state-of-the-art fitness assignment methods available for multiobjective optimization in order to analyze their ability to guide the search process in high-dimensional objective spaces.