Adaptive pattern search for large-scale optimization

Vincent Gardeux, Mahamed G. Mahamed, Rachid Chelouah, Patrick Siarry, Fred Glover

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

The emergence of high-dimensional data requires the design of new optimization methods. Indeed, conventional optimization methods require improvements, hybridization, or parameter tuning in order to operate in spaces of high dimensions. In this paper, we present a new adaptive variant of a pattern search algorithm to solve global optimization problems exhibiting such a character. The proposed method has no parameters visible to the user and the default settings, determined by almost no a priori experimentation, are highly robust on the tested datasets. The algorithm is evaluated and compared with 11 state-of-the-art methods on 20 benchmark functions of 1000 dimensions from the CEC’2010 competition. The results show that this approach obtains good performances compared to the other methods tested.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalApplied Intelligence
DOIs
StateAccepted/In press - Mar 11 2017
Externally publishedYes

Keywords

  • Adaptive methods
  • Continuous
  • High-dimension
  • Large-scale
  • Optimization
  • Pattern search
  • Scatter search

ASJC Scopus subject areas

  • Artificial Intelligence

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  • Cite this

    Gardeux, V., Mahamed, M. G., Chelouah, R., Siarry, P., & Glover, F. (Accepted/In press). Adaptive pattern search for large-scale optimization. Applied Intelligence, 1-12. https://doi.org/10.1007/s10489-017-0901-8