Advertisement

Landscape-Aware Constraint Handling Applied to Differential Evolution

  • Katherine M. Malan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11324)

Abstract

In real-world contexts optimisation problems frequently have constraints. Evolutionary algorithms do not naturally handle constrained spaces, so require constraint handling techniques to modify the search process. Based on the thesis that different constraint handling approaches are suited to different problem types, this study shows that the features of the problem can provide guidance in choosing appropriate constraint handling techniques for differential evolution. High level algorithm selection rules are derived through data mining based on a training set of problems on which landscape analysis is performed through sampling. On a set of different test problems, these rules are used to switch between constraint handling techniques during differential evolution search using on-line analysis of landscape features. The proposed landscape-aware switching approach is shown to out-perform the constituent constraint-handling approaches, illustrating that there is value in monitoring the landscape during search and switching to appropriate techniques depending on the problem characteristics. Results are also provided that show that the approach is fairly insensitive to parameter changes.

Keywords

Metaheuristics Landscape-aware search Fitness landscape Violation landscape Adaptive constraint handling 

References

  1. 1.
    Bischl, B., Mersmann, O., Trautmann, H., Preuß, M.: Algorithm selection based on exploratory landscape analysis and cost-sensitive learning. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 313–320 (2012)Google Scholar
  2. 2.
    Coello Coello, C.A.: A survey of constraint handling techniques used with evolutionary algorithms. Technical report, Laboratorio Nacional de Informática Avanzada (1999)Google Scholar
  3. 3.
    Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. Trans. Evol. Comput. 15(1), 4–31 (2011)CrossRefGoogle Scholar
  4. 4.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  5. 5.
    Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186(2–4), 311–338 (2000)CrossRefGoogle Scholar
  6. 6.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  7. 7.
    Liang, J., et al.: Problem definitions and evaluation criteria for the CEC 2006 competition on constrained real-parameter optimization. Technical report, Nanyang Technological University, Singapore (2006)Google Scholar
  8. 8.
    Malan, K.M., Oberholzer, J.F., Engelbrecht, A.P.: Characterising constrained continuous optimisation problems. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1351–1358, May 2015Google Scholar
  9. 9.
    Malan, K.M., Engelbrecht, A.P.: Particle swarm optimisation failure prediction based on fitness landscape characteristics. In: Proceedings of IEEE Swarm Intelligence Symposium, pp. 1–9 (2014)Google Scholar
  10. 10.
    Malan, K.M., Moser, I.: Constraint handling guided by landscape analysis in combinatorial and continuous search spaces. Evolutionary Computation p. Just Accepted (2018).  https://doi.org/10.1162/evco_a_00222
  11. 11.
    Mallipeddi, R., Suganthan, P.N.: Ensemble of constraint handling techniques. IEEE Trans. Evol. Comput. 14(4), 561–579 (2010)CrossRefGoogle Scholar
  12. 12.
    Mallipeddi, R., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2010 competition on constrained real-parameter optimization. Technical report, Nanyang Technological University, Singapore (2010)Google Scholar
  13. 13.
    Michalewicz, Z.: A survey of constraint handling techniques in evolutionary computation methods. Evol. Programm. 4, 135–155 (1995)Google Scholar
  14. 14.
    Muñoz, M.A., Kirley, M., Halgamuge, S.K.: The algorithm selection problem on the continuous optimization domain. In: Moewes, C., Nürnberger, A. (eds.) Computational Intelligence in Intelligent Data Analysis. Studies in Computational Intelligence, vol. 445, pp. 75–89. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-32378-2_6CrossRefGoogle Scholar
  15. 15.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  16. 16.
    Storn, R., Price, K.: Minimizing the real functions of the ICEC 1996 contest by differential evolution. In: Proceedings of the International Conference on Evolutionary Computation, pp. 842–844 (1996)Google Scholar
  17. 17.
    Suganthan, P.: Comparison of results on the 2010 CEC benchmark function set. Technical report, Nanyang Technological University, Singapore (2010)Google Scholar
  18. 18.
    Takahama, T., Sakai, S.: Constrained optimization by the \(\epsilon \) constrained differential evolution with gradient-based mutation and feasible elites. In: 2006 IEEE International Conference on Evolutionary Computation, pp. 1–8 (2006)Google Scholar
  19. 19.
    Takahama, T., Sakai, S.: Constrained optimization by \(\epsilon \) constrained particle swarm optimizer with \(\epsilon \) -level control. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds.) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol. 29, pp. 1019–1029. Springer, Heidelberg (2005).  https://doi.org/10.1007/3-540-32391-0_105

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Decision SciencesUniversity of South AfricaPretoriaSouth Africa

Personalised recommendations