Multiobjectivization from two objectives to four objectives in evolutionary multi-objective optimization algorithms

Abstract

Multiobjectivization is an interesting idea to solve a difficult single-objective optimization problem through its reformulation as a multiobjective problem. The reformulation is performed by introducing an additional objective function or decomposing the original objective function into multiple ones. Evolutionary multiobjective optimization (EMO) algorithms are often used to solve the reformulated problem. Such an optimization approach, which is called multiobjectivization, has been used to solve difficult single-objective problems in many studies. In this paper, we discuss the use of multiobjectivization to solve two-objective problems. That is, we discuss the idea of solving a two-objective optimization problem by reformulating it as a four-objective one. In general, the increase in the number of objectives usually makes the problem more difficult for EMO algorithms. Thus the handling of two-objective problems as four-objective ones may simply lead to the deterioration in the quality of obtained non-dominated solutions. However, in this paper, we demonstrate through computational experiments that better results are obtained for some two-objective test problems by increasing the number of objectives from two to four.