UEGO, an abstract clustering technique for multimodal global optimization

M. Jelasity, Pilar Martínez Ortigosa, Inmaculada García

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

In this paper, UEGO, a new general technique for accelerating and/or parallelizing existing search methods is suggested. The skeleton of the algorithm is a parallel hill climber. The separate hill climbers work in restricted search regions (or clusters) of the search space. The volume of the clusters decreases as the search proceeds which results in a cooling effect similar to simulated annealing. Besides this, UEGO can be effectively parallelized; the communication between the clusters is minimal. The purpose of this communication is to ensure that one hill is explored only by one hill climber. UEGO makes periodic attempts to find new hills to climb. Empirical results are also presented which include an analysis of the effects of the user-given parameters and a comparison with a hill climber and a GA.

Original languageEnglish
Pages (from-to)215-233
Number of pages19
JournalJournal of Heuristics
Volume7
Issue number3
DOIs
Publication statusPublished - May 2001

Fingerprint

Multimodal Optimization
Global optimization
Global Optimization
Clustering
Communication
Simulated annealing
Cooling
Skeleton
Search Methods
Simulated Annealing
Search Space
Decrease

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Theoretical Computer Science
  • Computational Theory and Mathematics

Cite this

UEGO, an abstract clustering technique for multimodal global optimization. / Jelasity, M.; Ortigosa, Pilar Martínez; García, Inmaculada.

In: Journal of Heuristics, Vol. 7, No. 3, 05.2001, p. 215-233.

Research output: Contribution to journalArticle

Jelasity, M. ; Ortigosa, Pilar Martínez ; García, Inmaculada. / UEGO, an abstract clustering technique for multimodal global optimization. In: Journal of Heuristics. 2001 ; Vol. 7, No. 3. pp. 215-233.
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