MOMS-GA: A multi-objective multi-state genetic algorithm for system reliability optimization design problems

Heidi A. Taboada, Jose F. Espiritu, David W. Coit

Research output: Contribution to journalArticlepeer-review

137 Scopus citations

Abstract

A custom genetic algorithm was developed and implemented to solve multiple objective multi-state reliability optimization design problems. Many real-world engineering design problems are multi-objective in nature, and among those, several of them have various levels of system performance ranging from perfectly functioning to completely failed. This multi-objective genetic algorithm uses the universal moment generating function approach to evaluate the different reliability or availability indices of the system. The components are characterized by having different performance levels, cost, weight, and reliability. The solution to the multi-objective multi-state problem is a set of solutions, known as the Pareto-front, from which the analyst may choose one solution for system implementation. Two illustrative examples are presented to show the performance of the algorithm; and the multi-objective formulation considered for both of them is tmaximization of system availability, and the minimization of both system cost, and weight.

Original languageEnglish (US)
Pages (from-to)182-191
Number of pages10
JournalIEEE Transactions on Reliability
Volume57
Issue number1
DOIs
StatePublished - Mar 2008

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Electrical and Electronic Engineering

Keywords

  • Genetic algorithms
  • Multi-objective optimization
  • Multi-state

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