Purpose – The purpose of this paper is threefold: to make explicitly clear the range of efficient multi-objective optimization algorithms which are available with kriging; to demonstrate a previously uninvestigated algorithm on an electromagnetic design problem; and to identify algorithms particularly worthy of investigation in this field. Design/methodology/approach – The paper concentrates ex...

In this paper, proper optimality concepts in vector optimization with variable ordering structures are introduced for the first time and characterization results via scalarizations are given. New type of scalarizing functionals are presented and their properties are discussed. The scalarization approach suggested in the paper does not require convexity and boundedness conditions.

DINAS is an interactive system to aid solving various multiobje~tive transshipment problems with facility focation using IBM-PC XT:AT or compatibles. DINAS utilizes the so-called aspiration-based (or reference point) approach to interactive handling of multiple objectives. In this approach the decision maker forms his/her requirements in terms of aspiration and reservation levels, i.e. specifie...

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...

This paper addresses a general multiobjective optimization problem. One of the most widely used methods of dealing with multiple conflicting objectives consists of constructing and optimizing a so-called achievement scalarizing function (ASF) which has an ability to produce any Pareto optimal or weakly/properly Pareto optimal solution. The ASF minimizes the distance from the reference point to ...

This work is devoted to examining inverse vector variational inequalities with constraints by means of a prominent nonlinear scalarizing functional. We show that inverse vector variational inequalities are equivalent to multiobjective optimization problems with a variable domination structure. Moreover, we introduce a nonlinear function based on a well-known nonlinear scalarization function. We...

In this work, we propose a novel methodology to identify parameters of compartmental epidemiological models. It is based on solving multi-objective optimization problem that consists in fitting some the model outputs real observations. First, according available data considered epidemic, define where are variables. Then, solved by considering particular algorithm called ParWASF-GA (Parallel Wei...

This paper presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approximating the Pareto Frontier, multiple reference points can be used instead of traditional techniques. These multiple reference points can easily be implemented in a parallel algorithmic framework. The reference points can be uniformly distributed wit...

Recently MOEA/D (multi-objective evolutionary algorithm based on decomposition) was proposed as a high-performance EMO (evolutionary multiobjective optimization) algorithm. MOEA/D has high search ability as well as high computational efficiency. Whereas other EMO algorithms usually do not work well on many-objective problems with four or more objectives, MOEA/D can properly handle them. This is...

Array syntax, existed in many languages, adds expressive power by allowing operations on and assignments to the array sections. When compiling to a uniprocessor machine, the array statement must be converted into a loop that maintains the correct semantics, by a process called scalarization. Scalarization presents a significant technical problem because an array assignment needs to be implement...