Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

Daniel Salazar, Claudio M. Rocco and Galván, Blas J.

Reliability Engineering and System Safety, 2006, vol. 91, issue 9, 1057-1070

Abstract: 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.

Keywords: Constrained optimization; MOEA; Multiple-objective optimization; Redundancy allocation and reliability optimization (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (30)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:91:y:2006:i:9:p:1057-1070

DOI: 10.1016/j.ress.2005.11.040

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