On the Scalarization of Vector Optimization Problems

Vector Optimization Problems have been intensively investigated by carrying out the analysis in the image space (namely, the space where the objective functions run) and several interesting results have been achieved. Here, exploiting some of these results and taking into account previous results, we present a scalarization method for Vector Optimization Problems. All the vector optima are found starting from the scalar problem and varying a parameter in the image space. In the special case of two objective functions, the method requires only one parameter and some calculus advantage are obtained. If the objective functions are convex, the method shrinks to a known one.