Splitting for optimization

Duan, Qibin and Kroese, Dirk P. (2016) Splitting for optimization. Computers and Operations Research, 73 119-131. doi:10.1016/j.cor.2016.04.015

Author Duan, Qibin
Kroese, Dirk P.
Title Splitting for optimization
Journal name Computers and Operations Research   Check publisher's open access policy
ISSN 0305-0548
Publication date 2016-09-01
Year available 2016
Sub-type Article (original research)
DOI 10.1016/j.cor.2016.04.015
Open Access Status Not Open Access
Volume 73
Start page 119
End page 131
Total pages 13
Place of publication Kidlington, Oxford United Kingdom
Publisher Pergamon Press
Collection year 2017
Language eng
Abstract The splitting method is a well-known method for rare-event simulation, where sample paths of a Markov process are split into multiple copies during the simulation, so as to make the occurrence of a rare event more frequent. Motivated by the splitting algorithm we introduce a novel global optimization method for continuous optimization that is both very fast and accurate. Numerical experiments demonstrate that the new splitting-based method outperforms known methods such as the differential evolution and artificial bee colony algorithms for many bench mark cases.
Keyword Evolutionary computation
Splitting method
Continuous optimization
Artificial bee colony
Differential evolution
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
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