Multi-objective evolutionary algorithms (MOEAs) can be mainly divided into set approximation methods and decomposition methods. The former approximates the Pareto front by the whole population directly, while the latter solves decomposed subproblems. The theoretical understanding of these methods is, however, quite insufficient. In this paper, we try to gain more understanding by investigating a combination of set approximation MOEAs with a sequential decomposition mechanism. Our theoretical analysis shows that, the combination achieves a better running time than the corresponding set approximation MOEAs by a factor $n$ (the problem size) on synthetic problems as well as the minimum spanning tree problem, which hints that the two types of MOEAs might be mutually complemental.
