Skip to main content

Advertisement

Log in

A Staged Continuous Tabu Search Algorithm for the Global Optimization and its Applications to the Design of Fiber Bragg Gratings

  • Published:
Computational Optimization and Applications Aims and scope Submit manuscript

Abstract

A novel staged continuous Tabu search (SCTS) algorithm is proposed for solving global optimization problems of multi-minima functions with multi-variables. The proposed method comprises three stages that are based on the continuous Tabu search (CTS) algorithm with different neighbor-search strategies, with each devoting to one task. The method searches for the global optimum thoroughly and efficiently over the space of solutions compared to a single process of CTS. The effectiveness of the proposed SCTS algorithm is evaluated using a set of benchmark multimodal functions whose global and local minima are known. The numerical test results obtained indicate that the proposed method is more efficient than an improved genetic algorithm published previously. The method is also applied to the optimization of fiber grating design for optical communication systems. Compared with two other well-known algorithms, namely, genetic algorithm (GA) and simulated annealing (SA), the proposed method performs better in the optimization of the fiber grating design.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. J. Andre, P. Siarry, and T. Dognon, “An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization,” Advances in Engineering software, vol. 32, pp. 49–60, 2001.

    Article  Google Scholar 

  2. J. Bae and J. Chun, “Design of fiber bragg gratings using the simulated annealing technique for an ideal WDM filter bank,” IEEE MILCOM 2000. 21st Century Military Communications Conference Proceedings, 2000, vol. 2, pp. 892–896.

  3. T. Erdogan, “Fiber grating spectra,” IEEE J. of Lightwave Technol, vol. 15, no. 8, pp. 1277–1294, 1997.

    Article  Google Scholar 

  4. F. Glover and M. Laguna, Tabu Search, Kluwer Academic Publishers, 1998.

  5. S.L. Ho, S.Y. Yang, G.Z. Ni, and H.C. Wong, “An improved Tabu search for the global optimizations of electromagnetic devices,” IEEE Transactions on Magnetics, vol. 37, pp. 3570–3574, 2001.

    Article  Google Scholar 

  6. C. Houck, J. Joines, and M. Kay, A Genetic Algorithm for Function Optimization: A Matlab Implementation, NCSU-IE TR 95-09, 1995. http://www.ie.ncsu.edu/mirage/GAToolBox/gaot/.

  7. L. Ingber, “Adaptive simulated annealing (ASA): Lessons learned,” Control and Cybernetics, vol. 25, pp. 33–54, 1996. http://www.ingber.com.

    Google Scholar 

  8. D. Karaboga, D.H. Horrocks, N. Karaboga, and A. Kalinli, “Designing digital FIR filters using tabu search algorithm,” in IEEE International Symposium on Circuits and Systems, Hong Kong, June 9–12, 1997, pp. 2236–2239.

  9. J.M. Machado, S. Yang, S.L. Ho, and P. Ni, “A common Tabu search algorithm for the global optimization of engineering problems,” Comput. Methods Appl. Mech. Engrg., vol. 190, pp. 3501–3510, 2001.

    Article  MathSciNet  Google Scholar 

  10. P. Siarry and G. Berthiau, “Fitting of Tabu search to optimize functions of continuous variables,” International J. for Numerical Methods in Engineering, vol. 40, pp. 2449–2457, 1997.

    Article  MathSciNet  Google Scholar 

  11. J. Skaar and K.M. Risvik, “A genetic Algorithm for the inverse problem in synthesis of fiber gratings,” IEEE J. of Lightwave Technol., vol. 16, no. 10, pp. 1928–1932, 1998.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Q. Ngo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zheng, R.T., Ngo, N.Q., Shum, P. et al. A Staged Continuous Tabu Search Algorithm for the Global Optimization and its Applications to the Design of Fiber Bragg Gratings. Comput Optim Applic 30, 319–335 (2005). https://doi.org/10.1007/s10589-005-4563-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10589-005-4563-9

Keywords

Navigation