Reliability optimization of series-parallel systems using a genetic algorithm

David Coit, Alice E. Smith

Research output: Contribution to journalArticle

539 Citations (Scopus)

Abstract

A problem-specific genetic algorithm (GA) is developed and demonstrated to analyze series-parallel systems and to determine the optimal design configuration when there are multiple component choices available for each of several k-out-of-n:G subsystems. The problem is to select components and redundancy-levels to optimize some objective function, given system-level constraints on reliability, cost, and/or weight. Previous formulations of the problem have implicit restrictions concerning the type of redundancy allowed, the number of available component choices, and whether mixing of components is allowed. GA is a robust evolutionary optimization search technique with very few restrictions concerning the type or size of the design problem. The solution approach was to solve the dual of a nonlinear optimization problem by using a dynamic penalty function. GA performs very well on two types of problems: 1) redundancy allocation originally proposed by Fyffe, Hines, Lee, and 2) randomly generated problem with more complex k-out-of-n:G configurations.

Original languageEnglish (US)
Pages (from-to)254-260, 263
JournalIEEE Transactions on Reliability
Volume45
Issue number2
DOIs
StatePublished - Jun 1 1996

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Redundancy
Genetic algorithms
Costs
Optimal design

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Hardware and Architecture
  • Software
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality

Cite this

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Reliability optimization of series-parallel systems using a genetic algorithm. / Coit, David; Smith, Alice E.

In: IEEE Transactions on Reliability, Vol. 45, No. 2, 01.06.1996, p. 254-260, 263.

Research output: Contribution to journalArticle

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