ON GENERALIZED-CONVEX CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION

In this paper, we consider multi-objective optimization problems involving not necessarily convex constraints and componentwise generalized-convex (e.g., semi-strictly quasi-convex, quasi-convex, or explicitly quasi-convex) vector-valued objective functions that are acting between a real linear topological pre-image space and a finite dimensional image space. For these multi-objective optimization problems, we show that the set of (strictly, weakly) efficient solutions can be computed completely by using at most two corresponding multi-objective optimization problems with a new feasible set that is a convex upper set of the original feasible set. Our approach relies on the fact that the original feasible set can be described using level sets of a certain realvalued function (a kind of penalization function). Finally, we apply our approach to problems where the constraints are given by a system of inequalities with a finite number of constraint functions.

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