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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationLarge-Scale Scientific Computing - 9th International Conference, LSSC 2013, Revised Selected Papers
PublisherSpringer Verlag
Pages281-288
Number of pages8
ISBN (Print)9783662438794
DOIs
StatePublished - 2014
Event9th International Conference on Large-Scale Scientific Computations, LSSC 2013 - Sozopol, Bulgaria
Duration: 3 Jun 20137 Jun 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8353 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th International Conference on Large-Scale Scientific Computations, LSSC 2013
Country/TerritoryBulgaria
CitySozopol
Period3/06/137/06/13

Keywords

  • Differential evolution
  • High dimensionality
  • Micro-algorithm
  • Reduced population

Fingerprint

Dive into the research topics of 'Micro differential evolution performance empirical study for high dimensional optimization problems'. Together they form a unique fingerprint.

Cite this