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

[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:1008202821328Search in Google Scholar

[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 - 523Search in Google Scholar

[3] K. Price, R. Storn, and J. Lampinen, Differential Evolution - A Practical Approach to Global Optimization, Springer-Verlag, Berlin, Heidelberg, 2005Search in Google Scholar

[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 - 18Search in Google Scholar

[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 - 67Search in Google Scholar

[6] J. Liu and J. Lampinen, A fuzzy adaptive differential evolution algorithm, Soft Computing, 9 (6), (2005), 448 - 46210.1007/s00500-004-0363-xSearch in Google Scholar

[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 - 1791Search in Google Scholar

[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.2008.927706Search in Google Scholar

[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 - 657Search in Google Scholar

[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 - 31Search in Google Scholar

[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.2016.01.004Search in Google Scholar

[12] F. Neri and V. Tirronen, Recent advances in differential evolution: a survey and experimental analysis, Artificial Intelligence Review, 33 (1-2), (2010), 61 - 106Search in Google Scholar

[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, 2002Search in Google Scholar

[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)00389-8Search in Google Scholar

[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.2010.5586330Search in Google Scholar

[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.2015.74Search in Google Scholar

[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.2014.79Search in Google Scholar

[18] Z. Michalewicz, Genetic Algorithms + Data structures = Evolution Programs, Springer-Verlag, New York, 199410.1007/978-3-662-07418-3Search in Google Scholar

[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.2013.055204Search in Google Scholar

[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 - 43Search in Google Scholar

[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 - 27Search in Google Scholar

[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.983836Search in Google Scholar

[23] R. Storn, Optimization of wireless communications applications using differential evolution, In SDR Technical Conference, Denver, (2007)Search in Google Scholar

[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 Scholar