Optimal Bi-objective redundancy allocation for systems reliability and risk management

Kannan Govindan, Ahmad Jafarian, Mostafa E. Azbari, Tsan Ming Choi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

43 Citations (Scopus)


In the big data era, systems reliability is critical to effective systems risk management. In this paper, a novel multiobjective approach, with hybridization of a known algorithm called NSGA-II and an adaptive population-based simulated annealing (APBSA) method is developed to solve the systems reliability optimization problems. In the first step, to create a good algorithm, we use a coevolutionary strategy. Since the proposed algorithm is very sensitive to parameter values, the response surface method is employed to estimate the appropriate parameters of the algorithm. Moreover, to examine the performance of our proposed approach, several test problems are generated, and the proposed hybrid algorithm and other commonly known approaches (i.e., Moga, NRGA, and NSGA-II) are compared with respect to four performance measures: 1) mean ideal distance; 2) diversification metric; 3) percentage of domination; and 4) data envelopment analysis. The computational studies have shown that the proposed algorithm is an effective approach for systems reliability and risk management.
Original languageEnglish
Article number7018913
Pages (from-to)1735-1748
Number of pages14
JournalIEEE Transactions on Cybernetics
Issue number8
Publication statusPublished - 1 Aug 2016


  • Meta-heuristic algorithms
  • multiobjective optimization
  • reliability optimization
  • systems risk management

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Optimal Bi-objective redundancy allocation for systems reliability and risk management'. Together they form a unique fingerprint.

Cite this