Have a personal or library account? Click to login
Gradual and Cumulative Improvements to the Classical Differential Evolution Scheme through Experiments Cover

Gradual and Cumulative Improvements to the Classical Differential Evolution Scheme through Experiments

Annals of West University of Timisoara - Mathematics and Computer Science
Volume 54 (2016): Issue 2 (December 2016)
By: George Anescu  
Open Access
|Dec 2016

References

  1. [1] K. Price and R. Storn, Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces, Journal of Global Optimization, 11 (4), (1997), 341 - 35910.1023/A:1008202821328
    Search in Google ScholarBack to article
  2. [2] R. Storn, On the usage of differential evolution for function optimization, Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), (1996), 519 - 523
    Search in Google ScholarBack to article
  3. [3] K. Price, R. Storn, and J. Lampinen, Differential Evolution - A Practical Approach to Global Optimization, Springer-Verlag, Berlin, Heidelberg, 2005
    Search in Google ScholarBack to article
  4. [4] J. Liu and J. Lampinen, On setting the control parameter of the differential evolution method, Proceedings of the 8th International Conference on Soft Computing (MENDEL), (2002), 11 - 18
    Search in Google ScholarBack to article
  5. [5] D. Zaharie, Critical values for the control parameters of differential evolution algorithms, Proceedings of the 8th International Conference on Soft Computing (MENDEL), (2002), 62 - 67
    Search in Google ScholarBack to article
  6. [6] J. Liu and J. Lampinen, A fuzzy adaptive differential evolution algorithm, Soft Computing, 9 (6), (2005), 448 - 46210.1007/s00500-004-0363-x
    Search in Google ScholarBack to article
  7. [7] A.K. Qin and P.N. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), 2005, 2, (2005), 1785 - 1791
    Search in Google ScholarBack to article
  8. [8] A.K. Qin, V.L. Huang, and P.N. Suganthan, Differential evolution algorithm with strategy adaptation for global numerical optimization, IEEE Transactions on Evolutionary Computation, 13 (2), (2008), 398 - 41710.1109/TEVC.2008.927706
    Search in Google ScholarBack to article
  9. [9] J. Brest, S. Greiner, B. Boskoviffc, M. Mernik, and V. Zumer, Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark functions, IEEE Transactions on Evolutionary Computation, 10 (6), (2006), 646 - 657
    Search in Google ScholarBack to article
  10. [10] S. Das and P.N. Suganthan, Differential Evolution: A Survey of the State-of- the-Art, IEEE Transactions on Evolutionary Computation, 15 (1), (2011), 4 - 31
    Search in Google ScholarBack to article
  11. [11] S. Das, P.N. Suganthan, and S.S. Mullick, Recent advances in differential evolution - An updated survey, Swarm and Evolutionary Computation, 27 (1), (2016), 1 - 3010.1016/j.swevo.2016.01.004
    Search in Google ScholarBack to article
  12. [12] F. Neri and V. Tirronen, Recent advances in differential evolution: a survey and experimental analysis, Artificial Intelligence Review, 33 (1-2), (2010), 61 - 106
    Search in Google ScholarBack to article
  13. [13] J.D. Pintér, Global Optimization: Software, Test Problems, and Applications, Ch. 15 in Handbook of Global Optimization, Volume 2, (Ed. P. M. Pardalos and H. F. Romeijn), Kluwer Academic Publishers, Dordrecht, Boston, London, 200210.1007/978-1-4757-5362-2_15
    Search in Google ScholarBack to article
  14. [14] K. Deb, An effcient constraint handling method for genetic algorithms, Computer Methods in Applied Mechanics and Engineering, 186 (2-4), (2000), 311 - 33810.1016/S0045-7825(99)00389-8
    Search in Google ScholarBack to article
  15. [15] R. Mallipeddi and P.N. Suganthan, Differential Evolution with Ensemble of Constraint Handling Techniques for solving CEC 2010 Benchmark Problems, In Proceedings IEEE Congress on Evolutionary Computation (CEC), 2010, 1 - 810.1109/CEC.2010.5586330
    Search in Google ScholarBack to article
  16. [16] G. Anescu and I. Prisecaru, NSC-PSO, a novel PSO variant without speeds and coefficients, Proceedings of 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2015, 460 - 46710.1109/SYNASC.2015.74
    Search in Google ScholarBack to article
  17. [17] G. Anescu, An imperialistic strategy approach to continuous global optimization problem, Proceedings of 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2014, 549 - 55610.1109/SYNASC.2014.79
    Search in Google ScholarBack to article
  18. [18] Z. Michalewicz, Genetic Algorithms + Data structures = Evolution Programs, Springer-Verlag, New York, 199410.1007/978-3-662-07418-3
    Search in Google ScholarBack to article
  19. [19] J. Momin and Yang Xin-She, A literature survey of benchmark functions for global optimization problems, Int. Journal of Mathematical Modelling and Numerical Optimisation, 4 (2), (2013), 150 - 19410.1504/IJMMNO.2013.055204
    Search in Google ScholarBack to article
  20. [20] M. Molga and C. Smutnicki, Test functions for optimization needs, http://www.robertmarks.org/Classes/ENGR5358/Papers/functions.pdf (last time accessed in February, 2016), (2005), 1 - 43
    Search in Google ScholarBack to article
  21. [21] A.J. Keane, Experiences with optimizers in structural design, Proceedings of the 1st Conf. on Adaptive Computing in Engineering Design and Control, University of Plymouth, UK, (1994), 14 - 27
    Search in Google ScholarBack to article
  22. [22] S.K. Mishra, Minimization of Keane's Bump Function by the Repulsive Particle Swarm and the Differential Evolution Methods, http://mpra.ub.uni-muenchen.de/3098/ (last time accessed in February, 2016), (2007), 1 - 1210.2139/ssrn.983836
    Search in Google ScholarBack to article
  23. [23] R. Storn, Optimization of wireless communications applications using differential evolution, In SDR Technical Conference, Denver, (2007)
    Search in Google ScholarBack to article
  24. [24] D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization, 39 (3), (2007), 459 - 471
    Search in Google ScholarBack to article
DOI: https://doi.org/10.1515/awutm-2016-0012 | Journal eISSN: 1841-3307 | Journal ISSN: 1841-3293
Journal RSS Feed
Language: English
Page range: 13 - 35
Submitted on: Sep 23, 2016
Accepted on: Feb 12, 2016
Published on: Dec 30, 2016
Published by: West University of Timisoara
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
Continuous Global Optimization Problem (CGOP),
Derivative-free Optimization,
Differential Evolution (DE),
DE/rand/1/bin,
Self-Adaptive DE variants,
Fast Self-Adaptive DE (FSA-DE)
Related subjects:
Mathematics,
General mathematics

© 2016 George Anescu, published by West University of Timisoara
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Previous articleVolume 54 (2016): Issue 2 (December 2016)Next article