Advertisement

Multi-strategy Differential Evolution

  • Anil Yaman
  • Giovanni Iacca
  • Matt Coler
  • George Fletcher
  • Mykola Pechenizkiy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10784)

Abstract

We propose the Multi-strategy Differential Evolution (MsDE) algorithm to construct and maintain a self-adaptive ensemble of search strategies while solving an optimization problem. The ensemble of strategies is represented as agents that interact with the candidate solutions to improve their fitness. In the proposed algorithm, the performance of each agent is measured so that successful strategies are promoted within the ensemble. We propose two performance measures, and show their effectiveness in selecting successful strategies. We then present three population adaptation mechanisms, based on sampling, clone-best and clone-multiple adaptation schemes. The MsDE with different performance measures and population adaptation schemes is tested on the CEC2013 benchmark functions and compared with basic DE and with Self-Adaptive DE (SaDE). Our results show that MsDE is capable of efficiently adapting the strategies and parameters of DE and providing competitive results with respect to the state-of-the-art.

Keywords

Continuous optimization Differential evolution Parameter control Strategy adaptation 

Notes

Acknowledgments

Open image in new window We would like to thank Dr. Samaneh Khoshrou from Eindhoven University of Technology for the informative discussion. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 665347.

References

  1. 1.
    Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning (1989)Google Scholar
  2. 2.
    Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution-an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)CrossRefGoogle Scholar
  4. 4.
    Neri, F., Tirronen, V.: Recent advances in differential evolution: a survey and experimental analysis. Artif. Intell. Rev. 33(1–2), 61–106 (2010)CrossRefGoogle Scholar
  5. 5.
    Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evol. Comput. 13(2), 398–417 (2009)CrossRefGoogle Scholar
  6. 6.
    Črepinšek, M., Liu, S.H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 35 (2013)zbMATHGoogle Scholar
  7. 7.
    Eiben, Á.E., Hinterding, R., Michalewicz, Z.: Parameter control in evolutionary algorithms. IEEE Trans. Evol. Comput. 3(2), 124–141 (1999)CrossRefGoogle Scholar
  8. 8.
    Karafotias, G., Hoogendoorn, M., Eiben, Á.E.: Parameter control in evolutionary algorithms: trends and challenges. IEEE Trans. Evol. Comput. 19(2), 167–187 (2015)CrossRefGoogle Scholar
  9. 9.
    Kramer, O.: Self-adaptive Heuristics for Evolutionary Computation, vol. 147. Springer, Heidelberg (2008)zbMATHGoogle Scholar
  10. 10.
    De Jong, K.A.: Analysis of the behavior of a class of genetic adaptive systems. Ph.D. thesis (1975)Google Scholar
  11. 11.
    Yaman, A., Hallawa, A., Coler, M., Iacca, G.: Presenting the ECO: evolutionary computation ontology. In: Squillero, G., Sim, K. (eds.) EvoApplications 2017. LNCS, vol. 10199, pp. 603–619. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55849-3_39 CrossRefGoogle Scholar
  12. 12.
    Hallawa, A., Yaman, A., Iacca, G., Ascheid, G.: A framework for knowledge integrated evolutionary algorithms. In: Squillero, G., Sim, K. (eds.) EvoApplications 2017. LNCS, vol. 10199, pp. 653–669. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-55849-3_42 CrossRefGoogle Scholar
  13. 13.
    Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer Science & Business Media, Heidelberg (2006)zbMATHGoogle Scholar
  14. 14.
    Iacca, G., Caraffini, F., Neri, F.: Multi-strategy coevolving aging particle optimization. Int. J. Neural Syst. 24(01), 1450008 (2014)CrossRefGoogle Scholar
  15. 15.
    Iacca, G., Mallipeddi, R., Mininno, E., Neri, F., Suganthan, P.N.: Super-fit and population size reduction in compact differential evolution. In: 2011 IEEE Workshop on Memetic Computing (MC), pp. 1–8, April 2011Google Scholar
  16. 16.
    Mallipeddi, R., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11(2), 1679–1696 (2011)CrossRefGoogle Scholar
  17. 17.
    Mallipeddi, R., Iacca, G., Suganthan, P.N., Neri, F., Mininno, E.: Ensemble strategies in compact differential evolution. In: 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 1972–1977, June 2011Google Scholar
  18. 18.
    Iacca, G., Neri, F., Caraffini, F., Suganthan, P.N.: A differential evolution framework with ensemble of parameters and strategies and pool of local search algorithms. In: Esparcia-Alcázar, A.I., Mora, A.M. (eds.) EvoApplications 2014. LNCS, vol. 8602, pp. 615–626. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-45523-4_50 Google Scholar
  19. 19.
    Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1785–1791. IEEE (2005)Google Scholar
  20. 20.
    Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Trans. Evol. Comput. 13(5), 945–958 (2009)CrossRefGoogle Scholar
  21. 21.
    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 Trans. Evol. Comput. 10(6), 646–657 (2006)CrossRefGoogle Scholar
  22. 22.
    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
  23. 23.
    De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. Evol. Comput. 6(3), 239–251 (2002)CrossRefGoogle Scholar
  24. 24.
    De Castro, L.N.: Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications. CRC Press (2006)Google Scholar
  25. 25.
    Liang, J., Qu, B., Suganthan, P., Hernández-Díaz, A.G.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report 201212, 3–18 (2013)Google Scholar
  26. 26.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bull. 1(6), 80–83 (1945)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.RWTH Aachen UniversityAachenGermany
  3. 3.University of Groningen/Campus FryslânLeeuwardenThe Netherlands

Personalised recommendations