C1,1 vector optimization problems and Riemann derivatives
Characterization of the stability set for non-differentiable fuzzy parametric optimization problems.
Characterizations of convex vector functions and optimization.
Clasificación en programación multiobjetivo.
En el presente trabajo, después de justificar lo importante que resulta para el decisor, en muchos casos, el poder obtener clasificaciones en el conjunto de objetivos de un problema de Porgramación Multiobjetivo, se hace un estudio algorítmico que permite agruparlos en función de ciertos niveles de conformidad.
Coercivity properties and well-posedness in vector optimization
This paper studies the issue of well-posedness for vector optimization. It is shown that coercivity implies well-posedness without any convexity assumptions on problem data. For convex vector optimization problems, solution sets of such problems are non-convex in general, but they are highly structured. By exploring such structures carefully via convex analysis, we are able to obtain a number of positive results, including a criterion for well-posedness in terms of that of associated scalar problems....
Coercivity properties and well-posedness in vector optimization*
This paper studies the issue of well-posedness for vector optimization. It is shown that coercivity implies well-posedness without any convexity assumptions on problem data. For convex vector optimization problems, solution sets of such problems are non-convex in general, but they are highly structured. By exploring such structures carefully via convex analysis, we are able to obtain a number of positive results, including a criterion for well-posedness in terms of that of associated scalar...
Combination of mobile agent and evolutionary algorithm to optimize the client transport services
This paper presents a migration strategy for a set of mobile agents (MAs) in order to satisfy customers' requests in a transport network, through a multimodal information system. In this context, we propose an optimization solution which operates on two levels. The first one aims to constitute a set of MAs building their routes, called Workplans. At this level, Workplans must incorporate all nodes, representing information providers in the multimodal network, in order to explore it completely....
Combining evolutionary algorithms and exact approaches for multi-objective knowledge discovery
An important task of knowledge discovery deals with discovering association rules. This very general model has been widely studied and efficient algorithms have been proposed. But most of the time, only frequent rules are seeked. Here we propose to consider this problem as a multi-objective combinatorial optimization problem in order to be able to also find non frequent but interesting rules. As the search space may be very large, a discussion about different approaches is proposed and a hybrid...
Comparison between different duals in multiobjective fractional programming
The present paper is a continuation of [2] where we deal with the duality for a multiobjective fractional optimization problem. The basic idea in [2] consists in attaching an intermediate multiobjective convex optimization problem to the primal fractional problem, using an approach due to Dinkelbach ([6]), for which we construct then a dual problem expressed in terms of the conjugates of the functions involved. The weak, strong and converse duality statements for the intermediate problems allow...
Cone convexity of measured set vector functions and vector optimization
Consistency-driven approximation of a pairwise comparison matrix
The pairwise comparison method is an interesting technique for building a global ranking from binary comparisons. In fact, some web search engines use this method to quantify the importance of a set of web sites. The purpose of this paper is to search a set of priority weights from the preference information contained in a general pairwise comparison matrix; i.e., a matrix without consistency and reciprocity properties. For this purpose, we consider an approximation methodology within a distance-based...
Continuous-time multiobjective optimization problems via invexity.