Advertisement

Solving Redundancy Optimization Problem with a New Stochastic Algorithm

  • Chun-Xia Yang
  • Zhi-Hua Cui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7077)

Abstract

In order to solve the real-world problem which named Cleveland heart disease classification problem, we used a new stochastic optimization algorithm that simulate the plant growing process. It employs the photosynthesis operator and phototropism operator to mimic photosynthesis and phototropism phenomenons, we call it briefly with APPM algorithm. For the plant growing process, photosynthesis is a basic mechanism to provide the energy from sunshine, while phototropism is an important character to guide the growing direction. In this algorithm, each individual is called a branch, and the sampled points are regarded as the branch growing trajectory. Phototropism operator is designed to introduce the fitness function value, as well as phototropism operator is used to decide the growing direction. Up to date, there is little applications. Therefore, in this paper, APPM is successfully applied to the redundancy optimization problem. The objective of the redundancy allocation problem is to select from available components and to determine an optimal design configuration to maximize system reliability. BP neural network is trained to calculate the objective fitness, while APPM is applied to check the best choice of feasibility of solution. One example is used to illustrate the effectiveness of APPM.

Keywords

Stochastic Algorithm Operation Research Letter Redundant Element Uncertain Function Redundancy Allocation Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Holland, J.H.: Adaptation in Natural and Arificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  2. 2.
    Kou, W., Prasad, V.R.: An annotated overview of systemreliability optimization. IEEE Transactions on Reliability 49, 176–197 (2000)CrossRefGoogle Scholar
  3. 3.
    Kou, W., Kim, T.: An overview of manufacturing yield and reliability modeling for semiconductor products. Proc. IEEE 87(8), 1329–1346 (1999)CrossRefGoogle Scholar
  4. 4.
    Chern, M.S.: On the computational complexity of reliability redundancy allocation in a series system. Operations Research Letters 11, 309–315 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Misra, K.B.: On optimal reliability design: A review. System Science 12, 5–30 (1986)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Melachrinoudis, E., Min, H.: A tabu search heuristic for solving the multi-depot, multi-vehicle, double request dial-a-ride problem faced by a healthcare organisation. Int. J. of Operational Research 10(2), 214–239 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Kohda, T., Inoue, K.: A reliability optimization method for complex systems with the criterion of local optimality. IEEE Trans. Reliability R-31(1), 109–111 (1982)CrossRefzbMATHGoogle Scholar
  8. 8.
    Shahul Hamid Khan, B., Govindan, K.: A multi-objective simulated annealing algorithm for permutation flow shop scheduling problem. Int. J. of Advanced Operations Management 3(1), 88–100 (2011)CrossRefGoogle Scholar
  9. 9.
    Baxter, L.A., Harche, F.: On the optimal assembly of series parallel systems. Operations Research Letters 11, 153–157 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Misra, K., Misra, V.: A procedure for solving general intege programming problems. Microelectronics and Reliability 34(1), 157–163 (1994)CrossRefGoogle Scholar
  11. 11.
    Sathappan, O.L., Chitra, P., Venkatesh, P., Prabhu, M.: Modified genetic algorithm for multiobjective task scheduling on heterogeneous computing system. Int. J. of Information Technology, Communications and Convergence 1(2), 146–158 (2011)CrossRefGoogle Scholar
  12. 12.
    Lu, J.-G., Li, H.-L., Chen, F.-X., Chen, L.: Combining strategy of genetic algorithm and particle swarm algorithm for optimum problem of RFID reader. Int. J. of Innovative Computing and Applications 3(2), 71–76 (2011)CrossRefGoogle Scholar
  13. 13.
    Ravi, V., Murty, B., Reddy, P.: Nonequilibrium simulate dannealing algorithm applied reliability optimization of complex systems. IEEE Trans. Reliability 46, 233–239 (1997)CrossRefGoogle Scholar
  14. 14.
    Glover, F., Laguna, M.: Tabu Search. Kluwer Academic Publishers (1997)Google Scholar
  15. 15.
    Martin, H.T., Howard, D.B., Mark, B.H.: Neural network design. PWS Publishing, Boston (1996)Google Scholar
  16. 16.
    Bai, D.S., Yun, W.Y., Cheng, S.W.: Redundancy optimization of k-out-of-n:G systems with common-cause failures. IEEE Trans. Reliability 40, 56–59 (1991)CrossRefzbMATHGoogle Scholar
  17. 17.
    Smith, A.L.: Oxford dictionary of biochemistry and molecular biology. Oxford University Press, Wellington (1997)Google Scholar
  18. 18.
    Bryant, D.A., Frigaard, N.U.: Prokaryotic photosynthesis and phototrophy illuminated. Trends in Microbiology 14(11), 488–496 (2006)CrossRefGoogle Scholar
  19. 19.
    Nealson, K.H., Conrad, P.G.: Life: past, present and future. Philosphical Transactions of the Royal Society, Part B, Biological Sciences 354(1392), 1923–1939 (1999)CrossRefGoogle Scholar
  20. 20.
    Field, C.B., Behrenfeld, M.J., Randerson, J.T., Falkowski, P.: Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281(5374), 237–240 (1998)CrossRefGoogle Scholar
  21. 21.
    Piao, Y.Z., Qiang, Y.: Comparison of a new model of light response of photosynthesis with traditional models. Journal of Shenyang Agricultural University 38(6), 771–775 (2007)Google Scholar
  22. 22.
    Boryczka, M., Slowinski, R.: Derivation of optimal decision algorithms from decisiont ables using rough sets. Bulletin of the Polish Academy of Sciences: Series Technical Sciences 36, 252–260 (1988)zbMATHGoogle Scholar
  23. 23.
    Ahn, B., Cho, S., Kim, C.: The integrated methodology of roughset theory and articial neural-network for business failure prediction. Expert Syst. Appl. 18(2), 65–74 (2000)CrossRefGoogle Scholar
  24. 24.
    Baxter, L.A., Harche, F.: On the optimal assembly of seriesparallel systems. Operations Research Letters 11 (1992)Google Scholar
  25. 25.
    Liu, B.: Theory and practice of uncertain programming, pp. 153–157. Springer-Verlag New York, LLC, New York (1992/2009) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Chun-Xia Yang
    • 1
  • Zhi-Hua Cui
    • 1
  1. 1.Complex System and Computational Intelligence LaboratoryTaiyuan University of Science and TechnologyChina

Personalised recommendations