UEGO, an Abstract Clustering Technique for Multimodal Global Optimization

Authors:
Márk Jelasity;Pilar Martínez Ortigosa;Inmaculada García
Affiliations:
Research Group on Artificial Intelligence, MTA-JATE, Szeged, Hungary. jelasity@inf.u-szeged.hu;Department of Computer Architecture and Electronics, University of Almería, Almería, Spain. ortigosa@ualm.es;Department of Computer Architecture and Electronics, University of Almería, Almería, Spain. igarcia@ualm.es
Venue:
Journal of Heuristics
Year:
2001

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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.