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Efficient hybrid methods for global continuous optimization based on simulated annealing
Helsinki School of Economics.
Department of Mathematical Information Technology, University of Jyväskylä.
Department of Mathematical Information Technology, University of Jyväskylä.
2006 (English)In: Computers & Operations Research, ISSN 0305-0548, E-ISSN 1873-765X, Vol. 33, no 4, 1102-1116 p.Article in journal (Refereed) Published
Abstract [en]

We introduce several hybrid methods for global continuous optimization. They combine simulated annealing and a local proximal bundle method. Traditionally, the simplest hybrid of a global and a local solver is to call the local solver after the global one, but this does not necessarily produce good results. Besides, using efficient gradient-based local solvers implies that the hybrid can only be applied to differentiable problems. We show several ways how to integrate the local solver as a genuine part of simulated annealing to enable both efficient and reliable solution processes. When using the proximal bundle method as a local solver, it is possible to solve even nondifferentiable problems. The numerical tests show that the hybridization can improve both the efficiency and the reliability of simulated annealing.

Place, publisher, year, edition, pages
2006. Vol. 33, no 4, 1102-1116 p.
Keyword [en]
Global optimization, Metaheuristics, Hybridization, Bundle methods
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-83655DOI: 10.1016/j.cor.2004.09.005ISI: 000232998100013OAI: oai:DiVA.org:kth-83655DiVA: diva2:498877
Note
QC 20120227Available from: 2012-02-12 Created: 2012-02-12 Last updated: 2017-12-07Bibliographically approved

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