Skip to main content
Log in

Adaptive trust region scheme for multi-objective optimization problem using Geršgorin circle theorem

  • Original Research
  • Published:
Journal of Applied Mathematics and Computing Aims and scope Submit manuscript

Abstract

In this article, a trust region algorithm is proposed to solve multi-objective optimization problem. A sequence of points is generated using Geršgorin Circle theorem with a modified secant equation. This sequence converges to a critical point of the problem. At every iteration, a common positive definite matrix is considered to take care of all the objective functions simultaneously and the radius of the trust region is obtained in an explicit form. Global convergence of the method is established with numerical support using a set of test problems.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Ansary, M.A.T., Panda, G.: A modified quasi-newton method for vector optimization problem. Optimization 64, 2289–2306 (2015)

    Article  MathSciNet  Google Scholar 

  2. Ansary, M.A.T., Panda, G.: A sequential quadratically constrained quadratic programming technique for a multi-objective optimization problem. Engineering Optimization 51(1), 22–41 (2019)

    Article  MathSciNet  Google Scholar 

  3. Ansary, M.A.T., Panda, G.: A sequential quadratic programming method for constrained multi-objective optimization problems. J. Appl. Math. Comput. 64, 379–397 (2020)

    Article  MathSciNet  Google Scholar 

  4. Deb, K.: Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Wiely India Pvt. Ltd., New Delhi, India (2003)

    Google Scholar 

  5. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiely India Pvt. Ltd., New Delhi, India (2003)

    MATH  Google Scholar 

  6. Ehrgott, M.: Multicriteria optimization. Berlin: Springer publication (2005)

    MATH  Google Scholar 

  7. Eichfelder, G.: An adaptive scalarization method in multiobjective optimization. SIAM J. Optim. 19(4), 1694–1718 (2009)

    Article  MathSciNet  Google Scholar 

  8. Fliege, F., Drummond, L.M.G., Svaiter, B.F.: Newton’s method for multiobjective optimization. SIAM J. Optim 20(2), 602–626 (2009)

  9. Fliege, J., Svaiter, F.V.: Steepest descent methods for multicriteria optimization. Math. Methods Oper. Res. 51(3), 479–494 (2000)

    Article  MathSciNet  Google Scholar 

  10. Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms.i.a unified formultion. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 28(1), 26–37 (1998)

  11. Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 10(5), 477–506 (2006)

    Article  Google Scholar 

  12. Hwang, C.L., Yoon, K.: Multiple attribute decision making: methods and applications a state-of-the-art survey. Springer , NewYork (1981)

  13. Jin, Y., Olhofer, M., Sendhoff, B.: Dynamic weighted aggregation for evolutionary multi-objective optimization: Why does it work and how? In Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation pp. 1042–1049 (2001)

  14. Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multi-objective optimization. Evolutionary Computation 10, 263–282 (2002)

    Article  Google Scholar 

  15. Lovison, A.: A synthetic approach to multiobjective optimization. arXiv preprint arXiv:1002.0093 (2010)

  16. Miettinen, K.: Nonlinear multiobjective optimization. Springer Boston: Kluwer (1999)

  17. Qu, S., Goh, M., Chan, F.T.S.: Quasi-newton methods for solving multiobjective optimization. Oper. Res. Lett. 39, 397–399 (2011)

    Article  MathSciNet  Google Scholar 

  18. Qu, S., Goh, M., Liang, B.: Trust region methods for solving multiobjective optimisation. Optim Methods Softw 28(4), 796–811 (2013)

    Article  MathSciNet  Google Scholar 

  19. Varga, R.S.: Geršgorin and his circles. Springer , NewYork (2010)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Geetanjali Panda.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bisui, N.K., Panda, G. Adaptive trust region scheme for multi-objective optimization problem using Geršgorin circle theorem. J. Appl. Math. Comput. 68, 2151–2172 (2022). https://doi.org/10.1007/s12190-021-01602-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12190-021-01602-0

Keywords

Mathematics Subject Classification

Navigation