• DocumentCode
    3719362
  • Title

    Simplified Swarm Optimization Algorithm for reliability redundancy allocation problems

  • Author

    Chia-Ling Huang;Wei-Chang Yeh

  • Author_Institution
    Department of Logistics and Shipping Management, Kainan Universit, Taoyuan 33857, Taiwan
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    An effective and simple solution methodology is applied and demonstrated to optimize the reliability redundancy allocation problems (RRAP) for the series-parallel system, the complex (bridge) system, and the overspeed protection of gas turbine system. The objective of the RRAP is the best known to maximize the system reliability for numerous decades. For this paper, in order to maximize the system reliability, it has to decide simultaneously the number of redundant components and the reliability of corresponding components in each subsystem with nonlinear constraints. This work is one difficulty for the RRAP. Hence, the RRAP is the mixed-integer programming problem with the nonlinear constraints that belongs to the NP-hard problem. In this paper, the Simplified Swarm Optimization (SSO) algorithm is proposed to solve the RRAP and improve computation efficiency for these NP-hard problems. The proposed SSO belongs to the category of Swarm Intelligence methods and is also an evolutionary computation method. Total three RRAP problems are successfully demonstrated by the proposed SSO algorithm. There are the comparisons of the experiment results among the proposed SSO algorithm with other available algorithms in this literature. On the average performance in the reliability of the three systems, the proposed SSO algorithm outperforms the previously best-known solutions.
  • Keywords
    "Reliability","Programming","Optimization","Continuous wavelet transforms","Cybernetics"
  • Publisher
    ieee
  • Conference_Titel
    Telecommunication Networks and Applications Conference (ITNAC), 2015 International
  • Type

    conf

  • DOI
    10.1109/ATNAC.2015.7366780
  • Filename
    7366780