Advertisement

Comparison of Adaptive Approaches for Differential Evolution

  • Karin Zielinski
  • Xinwei Wang
  • Rainer Laur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

The evaluation of optimization algorithms and especially the analysis of adaptive variants is often complicated because several features are modified concurrently. For Differential Evolution these features may be adaptation of parameters, adjustment of the strategy and addition of local search or other special operators. Thus, it is difficult to analyze which of these procedures is actually responsible for changes in the performance. Therefore, in this work several adaptive algorithms are studied in-depth by monitoring performance changes for individual components of these algorithms to examine their effectiveness. The results show among others that the performance can be significantly improved by employing strategy control.

Keywords

Local Search Sequential Quadratic Programming Adaptive Approach Trial Vector Feasible Individual 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution - A Practical Approach to Global Optimization. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  2. 2.
    Mezura-Montes, E., Velázquez-Reyes, J., Coello Coello, C.A.: A Comparative Study of Differential Evolution Variants for Global Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, Seattle, Washington, USA, pp. 485–492 (2006)Google Scholar
  3. 3.
    Gämperle, R., Müller, S.D., Koumoutsakos, P.: A Parameter Study for Differential Evolution. In: Grmela, A., Mastorakis, N. (eds.) Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 293–298. WSEAS Press (2002)Google Scholar
  4. 4.
    Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, V.: Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)CrossRefGoogle Scholar
  5. 5.
    Huang, V., Qin, A., Suganthan, P.: Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 324–331 (2006)Google Scholar
  6. 6.
    Zielinski, K., Laur, R.: Parameter Adaptation for Differential Evolution with Design of Experiments. In: Proceedings of the IASTED International Conference on Computational Intelligence, San Francisco, USA, pp. 212–217 (2006)Google Scholar
  7. 7.
    Zaharie, D.: Parameter Adaptation in Diffential Evolution by Controlling the Population Diversity. In: Proceedings of the 4th Workshop on Symbolic and Numeric Algorithms for Scientific Computing, Timisoara, Romania, pp. 385–397 (2002)Google Scholar
  8. 8.
    Abbass, H.A.: The Self-Adaptive Pareto Differential Evolution Algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation, Honolulu, HI, USA, pp. 831–836 (2002)Google Scholar
  9. 9.
    Ali, M.M., Törn, A.: Population Set Based Global Optimization Algorithms: Some Modifications and Numerical Studies. Computers and Operations Research 31(10), 1703–1725 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Liu, J., Lampinen, J.: A Fuzzy Adaptive Differential Evolution Algorithm. Soft Computing - A Fusion of Foundations, Methodologies and Applications 9(6), 448–462 (2005)zbMATHGoogle Scholar
  11. 11.
    Xue, F., Sanderson, A.C., Bonissone, P.P., Graves, R.J.: Fuzzy Logic Controlled Multi-objective Differential Evolution. In: Proceedings of the IEEE International Conference on Fuzzy Systems, Reno, NV, USA, pp. 720–725 (2005)Google Scholar
  12. 12.
    Teo, J.: Exploring Dynamic Self-adaptive Populations in Differential Evolution. Soft Computing 10(8), 673–686 (2006)CrossRefGoogle Scholar
  13. 13.
    Salman, A., Engelbrecht, A.P., Omran, M.G.: Empirical Analysis of Self-Adaptive Differential Evolution. European Journal of Operational Research 183(2), 785–804 (2007)CrossRefzbMATHGoogle Scholar
  14. 14.
    Price, K.V.: An Introduction to Differential Evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 79–108. McGraw-Hill, London (1999)Google Scholar
  15. 15.
    Price, K., Storn, R.: Website as in March 2008, http://www.icsi.berkeley.edu/~storn/code.html
  16. 16.
    Storn, R.: Designing Digital Filters with Differential Evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 109–125. McGraw-Hill, London (1999)Google Scholar
  17. 17.
    Deb, K.: An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering 186(2-4), 311–338 (2000)CrossRefzbMATHGoogle Scholar
  18. 18.
    Brest, J., Žumer, V., Maučec, M.S.: Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 919–926 (2006)Google Scholar
  19. 19.
    Brest, J., Žumer, V., Maučec, M.S.: Control Parameters in Self-Adaptive Differential Evolution. In: Proceedings of the Second International Conference on Bioinspired Optimization Methods and their Applications, Ljubljana, Slovenia, pp. 35–44 (2006)Google Scholar
  20. 20.
    Qin, A., Suganthan, P.: Self-adaptive Differential Evolution Algorithm for Numerical Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Edinburgh, UK, pp. 1785–1791 (2005)Google Scholar
  21. 21.
    Montgomery, D.C.: Design and Analysis of Experiments. John Wiley and Sons, Chichester (2001)Google Scholar
  22. 22.
    Zielinski, K., Laur, R.: Differential Evolution with Adaptive Parameter Setting for Multi-Objective Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, pp. 3585–3592 (2007)Google Scholar
  23. 23.
    Liang, J., Runarsson, T.P., Mezura-Montes, E., Clerc, M., Suganthan, P., Coello Coello, C.A., Deb, K.: Problem Definitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization. Technical report, Nanyang Technological University, Singapore (March 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Karin Zielinski
    • 1
  • Xinwei Wang
    • 1
  • Rainer Laur
    • 1
  1. 1.Institute for Electromagnetic Theory and Microelectronics (ITEM)University of BremenBremenGermany

Personalised recommendations