Advertisement

Paralleled Continuous Tabu Search Algorithm with Sine Maps and Staged Strategy for Solving CNOP

  • Shijin YuanEmail author
  • Yiwen Qian
  • Bin Mu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9530)

Abstract

Intelligent algorithms have been extensively applied in scientific computing. Recently, some researchers apply variable intelligent algorithms to solve conditional nonlinear optimal perturbation (CNOP) which is proposed to study the predictability of numerical weather and climate prediction. Among all the methods that have been studied, the principal components-based great deluge method (PCGD) showed remarkable effect and achieved the best result from the perspectives of CNOP magnitudes and patterns and efficiency. However, compared with adjoint-based method which is referred to as a benchmark, PCGD gets the smaller CNOP magnitude and cannot always get stable solutions. This paper proposes continuous tabu search algorithm with sine map and staged strategy (CTS-SS) to solve CNOP, then parallels CTS-SS with MPI. Based on continuous tabu search algorithm, CTS-SS uses sine chaotic maps to generate the initial candidates to avoid trapping in local optimum and then uses staged search strategy to accelerate the solving speed. To demonstrate the validity of CTS-SS, we take Zebiak-Cane model as a case to compare CTS-SS with the adjoint-based method and PCGD. Experimental results show that CTS-SS can efficiently obtain a satisfactory CNOP magnitude which is more close to the one computed with the adjoint-based method and larger than PCGD. Besides, CTS-SS can get more stable result than PCGD. In Addition, CTS-SS consumes similar time to PCGD and the adjoint-based method with 15 initial guess fields.

Keywords

Continuous tabu search algorithm Sine chaotic maps Staged strategy Parallelization CNOP Zebiak-Cane model 

Notes

Acknowledgments

In this paper, the research was sponsored by the Foundation of National Natural Science Fund of China (No. 41405097).

References

  1. 1.
    Mu, M., Duan, W.S.: A new approach to studying ENSO predictability: conditional nonlinear optimal perturbation. Chin. Sci. Bull. 48, 1045–1047 (2003)CrossRefGoogle Scholar
  2. 2.
    Duan, W.S., Mu, M.: Conditional nonlinear optimal perturbation as the optimal precursors for El Niño-Southern oscillation events. Geogr. Res. 109, 1–12 (2004)Google Scholar
  3. 3.
    Mu, M., Sun, L., Dijkstra, H.: The sensitivity and stability of the ocean’s thermocline circulation to finite amplitude freshwater perturbations. J. Phys. Oceanogr. 34, 2305–2315 (2004)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Wu, X.G., Mu, M.: Impact of horizontal diffusion on the nonlinear stability of thermohaline circulation in a modified box model. J. Phys. Oceanogr. 39, 798–805 (2009)CrossRefGoogle Scholar
  5. 5.
    Birgin, E.G., Martinez, J.E.M., Raydan, M.: No monotone spectral projected gradient methods on convex sets. Soc. Ind. Appl. Math. J. Optim. 10, 1196–1211 (2000)zbMATHGoogle Scholar
  6. 6.
    Mu, B., Zhang, L.L.: PCAGA: principal component analysis based genetic algorithm for solving conditional nonlinear optimal perturbation. In: International Joint Conference on Neural Networks (2015)Google Scholar
  7. 7.
    Wen, S.C., Mu, B., Yuan, S.J., Li, H.Y., Ren, J.H.: PCGD: principal components based great deluge method for solving CNOP. In: 2015 IEEE Congress on Evolutionary ComputationGoogle Scholar
  8. 8.
    Gao, W., Liu, S.: A modified artificial bee colony algorithm. Comput. Oper. Res. 39(3), 687–697 (2012)CrossRefzbMATHGoogle Scholar
  9. 9.
    Xu, H., Duan, W.S., Wang, J.C.: The tangent linear model and adjoint of a coupled ocean-atmosphere model and its application to the predictability of ENSO. In: Proceedings of IEEE International Conference on Geoscience and Remote Sensing Symposium, pp. 640–643 (2006)Google Scholar
  10. 10.
    Zebiak, S.E., Cane, M.A.: A model El Niño-Southern oscillation. Mon. Weather Rev. 115(10), 2262–2278 (1987)CrossRefGoogle Scholar
  11. 11.
    Yu, Y.S., Duan, W.S., Xu, H., Mu, M.: Dynamics of nonlinear error growth and season-dependent predictability of El Niño events in the ZebiakCane model. Q. J. Roy. Meteorol. Soc. 135(645), 2146–2160 (2009)CrossRefGoogle Scholar
  12. 12.
    Hu, N.: Tabu search method with random moves for globally optimal design. Int. J. Numer. Meth. Eng. 35(5), 1055–1070 (1992)CrossRefGoogle Scholar
  13. 13.
    Chelouah, R., Siarry, P.: Enhanced continuous tabu search: an algorithm for the global optimization of multiminima functions. In: Voss, S., Martello, S., Osman, I.H., Roucairol, C. (eds.) Meta-heuristics, Advances and Trends in Local Search Paradigms for Optimization, pp. 49–61. Kluwer Academic Publishers, Dordrecht (1999)CrossRefGoogle Scholar
  14. 14.
    Chen, L., Duan, W.S., Xu, H.: A SVD-based ensemble projection algorithm for calculating conditional nonlinear optimal perturbation. Sci. Chin. Earth Sci. 58, 385–394 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Software EngineeringTongji UniversityShanghaiChina

Personalised recommendations