For multiple-objective optimization problems, a common solution methodology is to determine a Pareto optimal set. Unfortunately, these sets are often large and can become difficult to comprehend and consider. Two methods are presented as practical approaches to reduce the size of the Pareto optimal set for multiple-objective system reliability design problems. The first method is a pseudo-ranki...

In this paper we present a new interactive procedure for multiObjective optimization, which is based on the use of a set of value functions as a preference model built by an ordinal regression method. The procedure is composed of two alternating stages. In the first stage, a representative sample of solutions from the Pareto optimal set (or from its approximation) is generated. In the second st...

This work evaluates two different approaches for multicriteria graph search problems using compromise preferences. This approach focuses search on a single solution that represents a balanced tradeoff between objectives, rather than on the whole set of Pareto optimal solutions. We review the main concepts underlying compromise preferences, and two main approaches proposed for their solution in ...

Prederivatives play an important role in the research of set optimization problems. First, we establish several existence theorems of prederivatives for γ-paraconvex set-valued mappings in Banach spaces with [Formula: see text]. Then, in terms of prederivatives, we establish both necessary and sufficient conditions for the existence of Pareto minimal solution of set optimization problems.

A Multiobjective optimization approach is used to propose a set of digital controllers for the throttle control benchmark that fulfills its specifications and requirements. The Multiobjective approach allows us to determine the best set of solutions that characterizes the Pareto set, where each solution has a different trade-off between the design objectives.

So far the focus of almost all multior many-objective performance metrics has been the convergence and distribution of solutions in the objective space (Pareto-surface). Pareto-surface metrics such as IGD, HV, and Spread are simple and provide knowledge about the overall performance of the solution set. However, these measures do not provide any insight into the distribution or spread of a solu...

Necessary conditions for Pareto optimality in multiobjective programming with subdifferentiable set functions are established in Theorem 12 of H. C. Lai and L. J. Ž . Lin J. Math. Anal. Appl. 132, 1988, 558]571 . In this paper, we establish some sufficient conditions under which a feasible solution of such a problem will be Pareto optimal provided that a weaker convexity requirement is satisfie...

Incorporating secondary structure information into the alignment process improves the quality of RNA sequence alignments. Instead of using fixed weighting parameters, sequence and structure components can be treated as different objectives and optimized simultaneously. The result is not a single, but a Pareto-set of equally optimal solutions, which all represent different possible weighting par...

This work presents an analytical approach to the design of a gesture vocabulary (GV) using multiobjectives for psycho-physiological and gesture recognition factors. A meta-heuristic approach is taken by decomposing the problem into two sub-problems: (i) finding the subsets of gestures that meet a minimal accuracy requirement, and (ii) matching gestures to commands to maximize the human factors ...

The automated shape optimization of an electrostatic micromotor with radial field is tackled. Two objectives in mutual contrast i.e. static torque and torque ripple, depending on two design variables, are considered. An innovative procedure for vector optimization which aims at obtaining as many optimal solutions as possible, is presented. To this end, a non-dominated sorting genetic algorithm ...