  Variable division and optimization (D\&O) is a frequently utilized algorithm
design paradigm in Evolutionary Algorithms (EAs). A D\&O EA divides a variable
into partial variables and then optimize them respectively. A complicated
problem is thus divided into simple subtasks. For example, a variable of
portfolio problem can be divided into two partial variables, i.e. the selection
of assets and the allocation of capital. Thereby, we optimize these two partial
variables respectively. There is no formal discussion about how are the partial
variables iteratively optimized and why can it work for both single- and
multi-objective problems in D\&O. In this paper, this gap is filled. According
to the discussion, an elitist selection method for partial variables in
multiobjective problems is developed. Then this method is incorporated into the
Decomposition-Based Multiobjective Evolutionary Algorithm (D\&O-MOEA/D). With
the help of a mathematical programming optimizer, it is achieved on the
constrained multiobjective portfolio problems. In the empirical study,
D\&O-MOEA/D is implemented for 20 instances and recent Chinese stock markets.
The results show the superiority and versatility of D\&O-MOEA/D on large-scale
instances while the performance of it on small-scale problems is also not bad.
The former targets convergence towards the Pareto front and the latter helps
promote diversity among the non-dominated solutions during the search process.
