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Splitting for optimization
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DOI：10.1016/j.cor.2016.04.015.png)
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Abstract
En 中文
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. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords:
Evolutionary computation
Splitting method
Continuous optimization
Artificial bee colony
Differential evolution
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