Advertisement

Self-Adaptive Differential Evolution for Dynamic Environments with Fluctuating Numbers of Optima

  • Mathys C. du Plessis
  • Andries P. Engelbrecht
Part of the Studies in Computational Intelligence book series (SCI, volume 433)

Abstract

In this chapter, we introduce the algorithm called: SADynPopDE, a self adaptive multi-population DE-based optimization algorithm, aimed at dynamic optimization problems in which the number of optima in the environment fluctuates over time. We compare the performance of SADynPopDE to those of two algorithms upon which it is based: DynDE and DynPopDE. DynDE extends DE for dynamic environments by utilizing multiple sub-populations which are encouraged to converge to distinct optima by means of exclusion. DynPopDE extends DynDE by: using competitive population evaluation to selectively evolve sub-populations, using a midpoint check during exclusion to determine whether sub-populations are indeed converging to the same optimum, dynamically spawning and removing sub populations, and using a penalty factor to aid the stagnation detection process. The use of self-adaptive control parameters into DynPopDE, allows a more effective algorithm, and to remove the need to fine-tune the DE crossover and scale factors.

Keywords

Differential Evolution Dynamic Environment Good Individual Dynamic Optimization Differential Evolution Algorithm 
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.
    A multi-population approach to dynamic optimization problems. In: Adaptive Computing in Design and Manufacturing, pp. 299–308. Springer (2000)Google Scholar
  2. 2.
    Blackwell, T.: Particle swarm optimization in dynamic environments. In: Evolutionary Computation in Dynamic and Uncertain Environments, pp. 29–49. Springer (2007)Google Scholar
  3. 3.
    Blackwell, T., Branke, J.: Multiswarm optimization in dynamic environments. Applications of Evolutionary Computing 3005, 489–500 (2004)CrossRefGoogle Scholar
  4. 4.
    Blackwell, T., Branke, J.: Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation 10(4), 459–472 (2006)CrossRefGoogle Scholar
  5. 5.
    Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Norwell (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Branke, J.: The moving peaks benchmark (2007), http://www.aifb.uni-karlsruhe.de/~jbr/MovPeaks/
  7. 7.
    Branke, J., Schmeck, H.: Designing evolutionary algorithms for dynamic optimization problems. In: Tsutsui, S., Ghosh, A. (eds.) Theory and Application of Evolutionary Computation: Recent Trends, pp. 239–262. Springer (2002)Google Scholar
  8. 8.
    Branke, J., Schmeck, H.: Designing evolutionary algorithms for dynamic optimization problems, pp. 239–262 (2003)Google Scholar
  9. 9.
    Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, 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
  10. 10.
    Brest, J., Zamuda, A., Boškovic, B., Maučec, M.S., Žumer, V.: Dynamic optimization using self-adaptive differential evolution. In: CEC 2009: Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, Piscataway, NJ, USA, pp. 415–422 (2009)Google Scholar
  11. 11.
    Carlisle, A., Dozier, G.: Tracking changing extrema with adaptive particle swarm optimizer. In: Proc. World Automation Congress, pp. 265–270 (2002)Google Scholar
  12. 12.
    Darwin, C.: The origin of species (1859)Google Scholar
  13. 13.
    du Plessis, M.C., Engelbrecht, A.P.: Improved differential evolution for dynamic optimization problems. In: IEEE Congress on Evolutionary Computation, CEC 2008, pp. 229–234 (June 2008)Google Scholar
  14. 14.
    du Plessis, M.C., Engelbrecht, A.P.: Differential evolution for dynamic environments with unknown numbers of optima. Submitted to Journal of Global Optimization (2010)Google Scholar
  15. 15.
    du Plessis, M.C., Engelbrecht, A.P.: Using competitive population evaluation in a differential evolution algorithm for dynamic environments. Submitted to European Journal of Operational Research (2010)Google Scholar
  16. 16.
    du Plessis, M.C., Engelbrecht, A.P.: Self-adaptive competitive differential evolution for dynamic environments. In: IEEE Symposium Series on Computational Intelligence, SSCI 2011, pp. 1–8 (April 2011)Google Scholar
  17. 17.
    Engelbrecht, A.P.: Computational Intelligence An Introduction, 2nd edn. John Wiley and Sons (2007)Google Scholar
  18. 18.
    Hu, X., Eberhart, R.C.: Adaptive particle swarm optimisation: detection and response to dynamic systems. In: Proceedings Congress on Evolutionary Computation, pp. 1666–1670 (2002)Google Scholar
  19. 19.
    Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments - a survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)CrossRefGoogle Scholar
  20. 20.
    Li, C., Yang, S.: A Generalized Approach to Construct Benchmark Problems for Dynamic Optimization. In: Li, X., Kirley, M., Zhang, M., Green, D., Ciesielski, V., Abbass, H.A., Michalewicz, Z., Hendtlass, T., Deb, K., Tan, K.C., Branke, J., Shi, Y. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 391–400. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Li, C., Yang, S., Nguyen, T.T., Yu, E.L., Yao, X., Jin, Y., Beyer, H.G., Suganthan, P.N.: University of Leicester, University of Birmingham, Nanyang Technological University, Technical Report (2008)Google Scholar
  22. 22.
    Li, X., Branke, J., Blackwell, T.: Particle swarm with speciation and adaptation in a dynamic environment. In: GECCO 2006: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 51–58. ACM, New York (2006)CrossRefGoogle Scholar
  23. 23.
    Mendes, R., Mohais, A.: Dynde: a differential evolution for dynamic optimization problems. In: Congress on Evolutionary Computation, pp. 2808–2815. IEEE (2005)Google Scholar
  24. 24.
    Mezura-Montes, E., Velázquez-Reyes, J., Coello, C.A.: A comparative study of differential evolution variants for global optimization. In: GECCO 2006: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 485–492. ACM, New York (2006)CrossRefGoogle Scholar
  25. 25.
    Morrison, R.W.: Designing Evolutionary Algorithms for Dynamic Environments. Springer (2004)Google Scholar
  26. 26.
    Omran, M.G.H., Salman, A., Engelbrecht, A.P.: Self-adaptive Differential Evolution. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 192–199. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  27. 27.
    Omran, M.G.H., Engelbrecht, A.P., Salman, A.: Bare bones differential evolution. European Journal of Operational Research 196(1), 128–139 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    Oppacher, F., Wineberg, M.: The shifting balance genetic algorithm: Improving the ga in a dynamic environment. In: Banzhaf, W., et al. (eds.) Genetic and Evolutionary Computation Conference (GECCO), vol. 1, pp. 504–510. Morgan Kaufmann, San Francisco (1999)Google Scholar
  29. 29.
    Parrott, D., Li, X.: A particle swarm model for tracking multiple peaks in a dynamic environment using speciation. In: Congress on Evolutionary Computation, pp. 98–103. IEEE (2004)Google Scholar
  30. 30.
    Price, K., Storn, R., Lampinen, J.: Differential evolution - A practical approach to global optimization. Springer (2005)Google Scholar
  31. 31.
    Storn, R.: On the usage of differential evolution for function optimization. In: Biennial Conference of the North American Fuzzy Information Processing Society, pp. 519–523. IEEE (1996)Google Scholar
  32. 32.
    Storn, R., Price, K.: Minimizing the real functions of the icec96 contest by differential evolution. In: IEEE Conference on Evolutionary Computation, pp. 842–844. IEEE (1996)Google Scholar
  33. 33.
    Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Ursem, R.K.: Multinational GA optimization techniques in dynamic environments. In: Whitley, D., Goldberg, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.-G. (eds.) Genetic and Evolutionary Computation Conference, pp. 19–26. Morgan Kaufmann (2000)Google Scholar
  35. 35.
    Zaharie, D., Zamfirache, F.: Diversity enhancing mechanisms for evolutionary optimization in static and dynamic environments. In: 3rd Romanian-Hungarian Joint Symposium on Applied Computational Intelligence, pp. 460–471 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mathys C. du Plessis
    • 1
  • Andries P. Engelbrecht
    • 2
  1. 1.Department of Computing SciencesNelson Mandela Metropolitan UniversityPort ElizabethSouth Africa
  2. 2.Department of Computer Science, School of Information TechnologyUniversity of PretoriaPretoriaSouth Africa

Personalised recommendations