Unidimensional search for solving continuous high-dimensional optimization problems

Vincent Gardeux, Rachid Chelouah, Patrick Siarry, Fred Glover

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

This paper presents a performance study of two versions of a unidimensional search algorithm aimed at solving high-dimensional optimization problems. The algorithms were tested on 11 scalable benchmark problems. The aim is to observe how metaheuristics for continuous optimization problems respond with increasing dimension. To this end, we report the algorithms' performance on the 50, 100, 200 and 500-dimension versions of each function. Computational results are given along with convergence graphs to provide comparisons with other algorithms during the conference and afterwards.

Original languageEnglish (US)
Title of host publicationISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications
Pages1096-1101
Number of pages6
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event9th International Conference on Intelligent Systems Design and Applications, ISDA 2009 - Pisa, Italy
Duration: Nov 30 2009Dec 2 2009

Other

Other9th International Conference on Intelligent Systems Design and Applications, ISDA 2009
CountryItaly
CityPisa
Period11/30/0912/2/09

Keywords

  • Metaheuristic
  • Optimization
  • Unidimensional

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Signal Processing
  • Software

Cite this

Gardeux, V., Chelouah, R., Siarry, P., & Glover, F. (2009). Unidimensional search for solving continuous high-dimensional optimization problems. In ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications (pp. 1096-1101). [5364201] https://doi.org/10.1109/ISDA.2009.191