  Many real-world optimization problems such as engineering design can be
eventually modeled as the corresponding multiobjective optimization problems
(MOPs) which must be solved to obtain approximate Pareto optimal fronts.
Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been
regarded as a significantly promising approach for solving MOPs. Recent studies
have shown that MOEA/D with uniform weight vectors is well-suited to MOPs with
regular Pareto optimal fronts, but its performance in terms of diversity
usually deteriorates when solving MOPs with irregular Pareto optimal fronts. In
this way, the solution set obtained by the algorithm can not provide more
reasonable choices for decision makers. In order to efficiently overcome this
drawback, we propose an improved MOEA/D algorithm by virtue of the well-known
Pascoletti-Serafini scalarization method and a new strategy of multi-reference
points. Specifically, this strategy consists of the setting and adaptation of
reference points generated by the techniques of equidistant partition and
projection. For performance assessment, the proposed algorithm is compared with
existing four state-of-the-art multiobjective evolutionary algorithms on
benchmark test problems with various types of Pareto optimal fronts. According
to the experimental results, the proposed algorithm exhibits better diversity
performance than that of the other compared algorithms. Finally, our algorithm
is applied to two real-world MOPs in engineering optimization successfully.
