The current Multi-Objective Evolutionary Algorithms (MOEAs) are mostly based on Pareto dominance relation to perform compare and ranking of individuals in the population. This kind of methods can solve two-objective optimization problems successfully, but their search ability and performance will deteriorate badly when the number of objectives exceeds four. Aiming to the problem, the influence is analyzed the number of objectives bring on optimization problems. And it is examined that NSGA2 performs in large-dimensional multi-objective optimization problems by a set of experiments, which is known as the representative of MOEAs baesd on Pareto ranking. Then the large-dimensional multi-objective evolutionary algorithms are surveyed by categories. Finally the proposed methods are evaluated and topics for future research are suggested.