Micro differential evolution performance empirical study for high dimensional optimization problems

Mauricio Olguin-Carbajal, J. Carlos Herrera-Lozada, Javier Arellano-Verdejo, Ricardo Barron-Fernandez, Hind Taud

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

7 Citas (Scopus)

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.

Idioma originalInglés
Título de la publicación alojadaLarge-Scale Scientific Computing - 9th International Conference, LSSC 2013, Revised Selected Papers
EditorialSpringer Verlag
Páginas281-288
Número de páginas8
ISBN (versión impresa)9783662438794
DOI
EstadoPublicada - 2014
Evento9th International Conference on Large-Scale Scientific Computations, LSSC 2013 - Sozopol, Bulgaria
Duración: 3 jun. 20137 jun. 2013

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen8353 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia9th International Conference on Large-Scale Scientific Computations, LSSC 2013
País/TerritorioBulgaria
CiudadSozopol
Período3/06/137/06/13

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