Skip to main content

A New Self-adaption Differential Evolution Algorithm Based Component Model

  • Conference paper
Advances in Computation and Intelligence (ISICA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6382))

Included in the following conference series:

Abstract

Finding a solution to constrained optimization problems (COPs) with differential evolution (DE) is a promising research issue. This paper proposes a novel algorithm to improve the original mutation and selection operators of DE. It explored some benefits from the component model and self-adaption mechanism, while solving the constrained optimization problems. Six benchmark functions about constraint problems are used in the experiment to evaluate the performance of the proposed algorithm. The experiment results demonstrate its effectiveness compared with other the current state-of-the art approaches in constraint optimization such as KM, SAFF and ISR.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Wang, Y., Cai, Z., Zhou, Y., Zeng, W.: An Adaptive Tradeoff Model for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation 12(1), 80–92 (2008)

    Article  Google Scholar 

  2. Michalewicz, Z., Janikow, C.Z.: Handling Constraints in Genetic Algorithms. In: The 4th International Conference on Genetic Algorithms (ICGA 1991), pp. 151–157. Morgan Kaufmann Publishers, California (1991)

    Google Scholar 

  3. Schoenauer, M., Michalewicz, Z.: Evolutionary Computation at the Edge of Feasibility. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 245–254. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  4. Michalewicz, Z., Nazhiyath, G.: Genocop III: A co-evolutionary algorithm for numerical optimization with nonlinear constraints. In: 2nd IEEE Conference Evolutionary Computation, vol. 5, pp. 647–651. IEEE Press, Los Alamitos (1995)

    Google Scholar 

  5. Schoenauer, M., Xanthakis, S.: Constrained GA Optimization. In: The 5th International Conference on Genetic Algorithms (ICGA 1993), pp. 573–580. Morgan Kauffman Publishers, California (1993)

    Google Scholar 

  6. Powell, D., Skolnick, M.M.: Using genetic algorithms in engineering design optimization with non-linear constraints. In: The 5th International Conference on Genetic Algorithms (ICGA 1993), pp. 424–431. Morgan Kauffman Publishers, California (1993)

    Google Scholar 

  7. Deb, K.: An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering 186(2/4), 311–338 (2000)

    Article  MATH  Google Scholar 

  8. Zhang, M., Luo, W., Wang, X.: Differential Evolution with Dynamic Stochastic Selection for Constrained Optimization. Information Sciences 178(15), 3043–3074 (2008)

    Article  Google Scholar 

  9. Mezura-Montes, Velazquez-Reyes, J., Coello Coello, C.A.: Promising infeasibility and multiple offspring incorporated to differential evolution for constrained optimization. In: GECCO, pp. 225–232 (2005)

    Google Scholar 

  10. Runarsson, T.P., Yao, X.: Search biases in constrained evolutionary optimization. J. IEEE Trans. Evolutionary Computation 35(2), 233–243 (2005)

    Google Scholar 

  11. Mezura-Montese, E., Colleo, C.A.C., Morales, T.: Simple feasibility rules and differential evolution for constrained optimization. In: The 3rd Mexican International Conference on Artificial Intelligence, pp. 707–716. Springer, Heidelberg (2004)

    Google Scholar 

  12. Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. J. IEEE Trans. Evolutionary Computation 4(3), 284–294 (2000)

    Article  Google Scholar 

  13. Wu, Y., Li, Y., Xu, X.: A Novel Component-Based Model and Ranking Strategy in Constrained Evolutionary Optimization. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds.) Advanced Data Mining and Applications. LNCS, vol. 5678, pp. 362–373. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Storn, R., Price, K.: Minimizing the real functions of the ICEC 1996 contest by differential evolution. In: IEEE International Conference on Evolutionary Computation, pp. 842–844. IEEE Press, Nagoya (1996)

    Chapter  Google Scholar 

  15. Storn, R., Price, K.: Differential evolution-A simple and efficient adaptive scheme for global optimization over continuous spaces. University of California, Berkeley (2006)

    Google Scholar 

  16. Koziel, S., Michalewicz, Z.: Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. J. Evolutionary Computation 7, 19–44 (1999)

    Article  Google Scholar 

  17. Farmani, R., Wright, J.A.: Self-adaptive fitness formulation for constrained optimization. J. IEEE Trans. Evolutionary Computation 7(5), 445–455 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Li, S., Li, Y. (2010). A New Self-adaption Differential Evolution Algorithm Based Component Model. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2010. Lecture Notes in Computer Science, vol 6382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16493-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-16493-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16492-7

  • Online ISBN: 978-3-642-16493-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics