Solving Nonlinear Constrained Optimization Problems Using Invasive Weed Optimization

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 308)

Abstract

Many real-world problems are constrained optimization problems. In solving nonlinear constrained optimization problems, penalty function method has been the popular approach. The performance of invasive weed optimization (IWO) with multistage penalty function is discussed in this paper. The proposed IWO is performed for six well-known problems, and results are reported. The obtained results demonstrate that IWO outperformed than other evolutionary algorithms.

Keywords

Invasive weed optimization Multistage penalty function Nonlinear constrained optimization problems 

References

  1. 1.
    Floudas, C.A., Pardalos, P.M.: A collection of test problems for constraints global optimization algorithms. In: Goos, G., Hartmanis, J. (eds.) LNCS, vol. 455. Springer, Heidelberg (1990)Google Scholar
  2. 2.
    Hock, W., Schittkowski, K.: Test examples for nonlinear programming codes. In: Beckmann, M., Kunzi, H. P. (eds.) LNEMS, vol. 187. Springer, Heidelberg (1981)Google Scholar
  3. 3.
    Yang, J.M., Chen, Y., Horng, J.T., Kao, C.Y.: Applying family competition to evolution strategies for constrained optimization. In: Peter, J.A., Robert, G.R., John, R.M., Russ, E. (eds.) USA 1997. LNCS, vol. 1231, pp. 201–211. Springer, Heidelberg (1997)Google Scholar
  4. 4.
    Pappula, L., Ghosh, D.: Large array synthesis using invasive weed optimization. In: IEEE conference on microwave and photonics, pp. 1–6. IEEE Press, IndiaGoogle Scholar
  5. 5.
    Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1, 355–366 (2006)CrossRefGoogle Scholar
  6. 6.
    Homaifar, A., Lia, A.H., Qi, X.: Constrained optimization via genetic algorithms. Simulation 2, 242–254 (1994)CrossRefGoogle Scholar
  7. 7.
    Joines, J.A., Houck, C.R.: On the use of non-stationary function to solve nonlinear constrained optimization problems with GA’s. In: IEEE Conference on evolutionary computation, pp. 579–584. IEEE Press, Orlando, Florida (1994)Google Scholar
  8. 8.
    Mezura, E.: Alternative to handle constraints in evolutionary optimization. Ph.D. thesis, CINVESTAV-IPN, Mexico (2004)Google Scholar
  9. 9.
    Rao, S.S.: Optimization: Theory and Applications. Wiley Eastern Limited, New York (1977)Google Scholar
  10. 10.
    Parsopoulos, K. E., Vrahatis, M.N.: Particle swarm optimization method for constrained optimization problems. In: 2nd euro-international symposium on computational intelligence, pp. 214–220. IOS Press, Kosice (2002)Google Scholar
  11. 11.
    Himmelblau, D.M.: Applied Nonlinear Programming. McGraw-Hill, New York (1972)MATHGoogle Scholar
  12. 12.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer AI Series, New York (1992)CrossRefMATHGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.School of Basic SciencesIndian Institute of Technology BhubaneswarBhubaneswarIndia

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