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, 200210.1007/978-1-4757-5362-2_15Search 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