Skip to main content
Log in

A filled function which has the same local minimizer of the objective function

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

Auxiliary function methods have been considered to be practical approaches for finding the global minimizer of multi-model functions.Filled function methods, as a typical representative of auxiliary function methods, obtain a global minimizer by minimizing the objective function and the filled function cyclically. In order to improve the efficiency of the filled function, this paper presents a new filled function which has the same local minimizers of the objective function, and these minimizers are all better than the current minimizer of the objective function. Therefore, it does not need to minimize the objective function except for the first iteration in the filled function method. Additionally, the proposed filled function excludes some disadvantages of conventional filled functions and a classical local optimization method can be applied directly to the new filled function to obtain a better minimizer of the original problem. Finally, numerical experiments are made and the results show the effectiveness of the proposed method.

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. Ge, R.: A filled function method for finding a global minimizer of a function of several variables. Math. Program. 46, 191–204 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  2. Ge, R.: The theory of filled function method for finding global minimizers of nonlinearly constrained minimization problems. J. Comput. Math. 5, 1–9 (1987)

    MathSciNet  MATH  Google Scholar 

  3. Levy, A.V., Montalvo, A.: The tunneling algorithm for the global minimization of functions. SIAM J. Sci. Stat. Comput. 6, 15–29 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  4. Wang, Y., Fang, W., Wu, T.: A cut-peak function method for global optimization. J. Comput. Appl. Math. 230, 135–142 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  5. Branin, F.H.: Widely convergent method for finding multiple solutions of simultaneous nonlinear equations. IBM J. Res. Dev. 16, 504–522 (1972)

    Article  MathSciNet  MATH  Google Scholar 

  6. Snyman, J.A., Fatti, L.P.: A multi-start global minimization algorithm with dynamic search trajectories. J. Optim. Theory Appl. 54, 121–141 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  7. Basso, P.: Iterative methods for the localization of the global maximum. SIAM J. Numer. Anal. 19, 781–792 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  8. Mladineo, R.H.: An algorithm for finding the global maximum of a multimodal, multivariate function. Math. Program. 34, 188–200 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  9. Lin, H., Gao, Y., Wang, Y.: A continuously differentiable filled function method for global optimization. Numer. Algorithms 66, 511–523 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  10. Leung, Y.W., Wang, Y.: Multiobjective programming using uniform design and genetic algorithm. IEEE Trans. Syst. Man Cybern. Part C 30, 293–304 (2000)

    Article  Google Scholar 

  11. Dang, C., Ma, W., Liang, J.: A deterministic annealing algorithm for approximating a solution of the min-bisection problem. Neural Netw. 22, 58–66 (2009)

    Article  MATH  Google Scholar 

  12. Li, X., Yao, X.: Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans. Evolut. Comput. 16, 210–224 (2012)

    Article  Google Scholar 

  13. Mininno, E., Neri, F., Cupertino, F., Naso, D.: Compact differential evolution. IEEE Trans. Evolut. Comput. 15, 32–54 (2011)

    Article  Google Scholar 

  14. Woon, S.F., Rehbock, V.: A critical review of discrete filled function methods in solving nonlinear discrete optimization problems. Appl. Math. Comput. 217, 25–41 (2010)

    MathSciNet  MATH  Google Scholar 

  15. Ling, A.F., Xu, C.X., Xu, F.M.: A discrete filled function algorithm for approximate global solutions of max-cut problems. J. Comput. Appl. Math. 220, 643–660 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  16. Gao, Y., Yang, Y., You, M.: A new filled function method for global optimization. Appl. Math. Comput. 268, 685–695 (2015)

    MathSciNet  MATH  Google Scholar 

  17. El-Gindy, T.M., Salim, M.S., Ahmed, A.I.: A new filled function method applied to unconstrained global optimization. Appl. Math. Comput. 273, 1246–1256 (2016)

    MathSciNet  MATH  Google Scholar 

  18. Liu, X.: Finding global minima with a computable filled function. J. Glob. Optim. 19, 151–161 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  19. Liu, X.: A class of continuously differentiable filled functions for global optimization. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 38, 38–47 (2008)

    Article  Google Scholar 

  20. Zhang, L.S., Ng, C.K., Li, D., Tian, W.W.: A new filled function method for global optimization. J. Glob. Optim. 28, 17–43 (2004)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Nature Science Foundation of China (Nos. 11161001, 61402350, 61375121), the Research Foundation of Jinling Institute of Technology (Nos. jit-b-201314, jit-n-201309, jit-rcyj-201505) and the Funds for Nanjing Creative Team of Swarm Computing & Smart Software Led by Prof. S. Su.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hongwei Lin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, H., Gao, Y., Wang, X. et al. A filled function which has the same local minimizer of the objective function. Optim Lett 13, 761–776 (2019). https://doi.org/10.1007/s11590-018-1275-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-018-1275-5

Keywords

Navigation