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Search for the most reliable network of fixed connectivity using genetic algorithm

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Published:12 July 2014Publication History

ABSTRACT

Reliability is one of the important measures of how well a system meets its design objective, and mathematically is the probability that a system will perform satisfactorily for a given period of time. When the system is described by a network of N components (nodes) and L connections (links), the reliability of the system becomes a network design problem that is an NP-hard combinatorial optimization problem. In this paper, genetic algorithm is applied to find the most reliable connected network with the same connectivity, (i.e. with given N and L). The accuracy and efficiency of genetic algorithm in the search of the most reliable network(s) of same connectivity is verified by exhaustive search. Our results not only demonstrate the efficiency of our algorithm for optimization problem for graphs, but also suggest that the most reliable network will have high symmetry.

References

  1. Francisco J. Samaniego, System signatures and their applications in engineering reliability, Boston, MA: Springer Science+Business Media, LLC, 2007.Google ScholarGoogle Scholar
  2. Kuo, W., Prasad, V. R., Tillman, F. A., Hwang, C.: Optimal Reliability Design, Cambridge University Press, Cambridge, MA (2001).Google ScholarGoogle Scholar
  3. Colbourn, C. J.: The Combinatorics of Network Reliability. Oxford University Press, Oxford (1987). Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Garey, M. R. and Johnson, D. S. (1979) Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman and Co.. San Francisco, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Aggamal, K. K. and Rai, S. (1981) Reliability evaluation in computer communication networks. IEEE Transactions on Reliability, R-30, 32--35.Google ScholarGoogle Scholar

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        • Published in

          cover image ACM Conferences
          GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
          July 2014
          1524 pages
          ISBN:9781450328814
          DOI:10.1145/2598394

          Copyright © 2014 Owner/Author

          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 12 July 2014

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          Acceptance Rates

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