Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/72725
Título: Optimization of constrained multiple-objective reliability problems using evolutionary algorithms
Autores/as: Salazar, Daniel
Rocco, Claudio M.
Galván González, Blas José 
Clasificación UNESCO: 1207 Investigación operativa
Palabras clave: Design
Constrained optimization
Moea
Multiple-objective optimization
Redundancy allocation and reliability optimization
Fecha de publicación: 2006
Publicación seriada: Reliability Engineering and System Safety 
Resumen: This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature.
URI: http://hdl.handle.net/10553/72725
ISSN: 0951-8320
DOI: 10.1016/j.ress.2005.11.040
Fuente: Reliability Engineering & System Safety [ISSN 0951-8320], v. 91 (9), p. 1057-1070, (Septiembre 2006)
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