Autores
Olguín Carbajal Mauricio
Herrera Lozada Juan Carlos
Arellano Verdejo Javier
Barrón Fernández Ricardo
Título Micro Differential Evolution Performance Empirical Study for High Dimensional Optimization Problems
Tipo Congreso
Sub-tipo SCOPUS
Descripción Lecture Notes in Computer Science; 9th International Conference on Large-Scale Scientific Computations, LSSC 2013
Resumen 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.
Observaciones Code 106191;
Lugar Sozopol
País Bulgaria
No. de páginas 281-288
Vol. / Cap. Vol. 8353 LNCS, 2014,
Inicio 2013-06-03
Fin 2013-06-07
ISBN/ISSN 978-366243879-4