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dc.description.abstractIn recent years, there has been an increasing interest in the multi-objective uncertain optimization, discussed in the framework of the interval-valued optimization, as a consequence theoretical developments have achieved significant results as theorems analogous to the conditions of Karush Kunt Tucker, but computational developments are still incipient. This paper makes an extension of Strength Pareto Evolutionary Algorithm 2 - SPEA2 - and Multi-objective Particle Swarm Optimization - MOPSO -, which ones are traditionally used in multi-objective optimization, these are modified to the case of multi-objective uncertain optimization, where the model uses the interval-valued optimization as shown by Wu [?, ?, ?], these new algorithms have arithmetic advantage in the image set of the objective function. At the end, numerical examples are shown where they applied the algorithms
dc.publisherUniversidad EAFITspa
dc.titleComputational methods for solving multi-objective uncertain optimization problemsspa
dc.publisher.programGrupo de Investigación Análisis Funcional y Aplicacionesspa
dc.publisher.departmentUniversidad EAFIT. Escuela de Ciencias y Humanidades. Grupo de Investigación Análisis Funcional y Aplicacionesspa
dc.type.localDocumento de trabajo de investigaciónspa
dc.rights.localAcceso restringidospa
dc.contributor.authorPuerta Yepes, María Eugenia
dc.contributor.authorCano Cadavid, Andrés Felipe

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