Scalarization approaches are the simplest methods for solving the multiobjective problems. The idea of scalarization is based on decomposition of multiobjective problems into single objective sub-problems. Every one of these sub-problems can be solved in a parallel manner since they are independent with each other. Hence, as a scalarization approach, systematically modification on the desirabil...

We study spontaneous scalarization in Scalar-Tensor boson stars. We find that scalarization does not occur in stars whose bosons have no self-interaction. We introduce a quartic self-interaction term into the boson Lagrangian and show that when this term is large, scalarization does occur. Strong self-interaction leads to a large value of the compactness (or sensitivity) of the boson star, a ne...

‎a common approach to determine efficient solutions of a multiple objective optimization problem‎ ‎is reformulating it to a parameter dependent scalar optimization problem‎. ‎this reformulation is called scalarization approach‎. here, a well-known scalarization approach named pascoletti-serafini scalarization is considered‎. first, some difficulties of this scalarization are discussed and then ...

a problem that sometimes occurs in multiobjective optimization is the existence of a large set of fairly effcient solutions. hence, the decision making based on selecting a unique preferred solution is diffcult. considering models with fair b-effciency relieves some of the burden from the decision maker by shrinking the solution set, since the set of fairly b-efficient solutions is contained with...

The aim of this paper is to investigate several inherited properties of convexity for set-valued maps and develop computational procedure based on such inherited properties. In this paper, we introduced two types of characteristic functions by using Tchebyshev scalarization, and defined four types of scalarization functions to characterize the images of set-valued maps.

In this paper, without assumption of monotonicity, we study the compactness and the connectedness of the weakly efficient solutions set to vector equilibrium problems by using scalarization method in locally convex spaces. Our results improve the corresponding results in [X. H. Gong, Connectedness of the solution sets and scalarization for vector equilibrium problems, J. Optim. Theory Appl. 133...

This paper presents a new method for scalarization of nonlinear multi-objective optimization problems. We introduce a special class of monotonically increasing sublinear scalarizing functions and show that the scalar optimization problem constructed by using these functions, enables to compute complete set of weakly efficient, efficient, and properly efficient solutions of multi-objective optim...

It is known that vector variational inequality models arises from vector optimization and vector traac equilibria. In this paper, existence results of a solution for a generalized pre-vector variational inequality and a generalized pre-quasi-vector variational inequality are established. The main condition used is that the underlying vector-valued function is assumed to be cone-quasi-convex. Th...

Our goal in this talk is to present unifying concepts for both stochastic and robust optimization problems involving infinite uncertainty sets. We apply methods from vector optimization in general spaces, set-valued optimization and scalarization techniques to develop a unified characterization of different concepts of robust optimization and stochastic programming. These methods provide new in...