TY - GEN
T1 - Micro differential evolution performance empirical study for high dimensional optimization problems
AU - Olguin-Carbajal, Mauricio
AU - Herrera-Lozada, J. Carlos
AU - Arellano-Verdejo, Javier
AU - Barron-Fernandez, Ricardo
AU - Taud, Hind
PY - 2014
Y1 - 2014
N2 - This paper presents an empirical study of a micro Differential Evolution algorithm (micro-DE) performance versus a canonical Differential Evolution (DE) algorithm performance. Micro-DE is a DE algorithm with reduced population and some other differences. This paper's objective is to show that our micro-DE outperforms the canonical DE for large scale optimization problems by using a test bed consisting of 20 complex functions with high dimensionality for a performance comparison between the algorithms. The results show two important points; first, the relevance of an accurate set of the optimization algorithms parameters regarding the problem itself. Second, we demonstrate the superior performance of our micro-DE with respect to DE in 19 out 20 tested functions. In some functions, the difference is up to seven orders of magnitude. Also, we show that micro-DE is better statistically than a simple DE and an adjusted DE for high dimensionality. In several problems where DE is used, micro-DE is highly recommended, as it achieves better results and statistic behavior without much change in code.
AB - This paper presents an empirical study of a micro Differential Evolution algorithm (micro-DE) performance versus a canonical Differential Evolution (DE) algorithm performance. Micro-DE is a DE algorithm with reduced population and some other differences. This paper's objective is to show that our micro-DE outperforms the canonical DE for large scale optimization problems by using a test bed consisting of 20 complex functions with high dimensionality for a performance comparison between the algorithms. The results show two important points; first, the relevance of an accurate set of the optimization algorithms parameters regarding the problem itself. Second, we demonstrate the superior performance of our micro-DE with respect to DE in 19 out 20 tested functions. In some functions, the difference is up to seven orders of magnitude. Also, we show that micro-DE is better statistically than a simple DE and an adjusted DE for high dimensionality. In several problems where DE is used, micro-DE is highly recommended, as it achieves better results and statistic behavior without much change in code.
KW - Differential evolution
KW - High dimensionality
KW - Micro-algorithm
KW - Reduced population
UR - http://www.scopus.com/inward/record.url?scp=84904116078&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-43880-0_31
DO - 10.1007/978-3-662-43880-0_31
M3 - Contribución a la conferencia
AN - SCOPUS:84904116078
SN - 9783662438794
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 281
EP - 288
BT - Large-Scale Scientific Computing - 9th International Conference, LSSC 2013, Revised Selected Papers
PB - Springer Verlag
T2 - 9th International Conference on Large-Scale Scientific Computations, LSSC 2013
Y2 - 3 June 2013 through 7 June 2013
ER -