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Parallel memetic algorithm for training recurrent neural networks for the energy efficiency problem

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Mar 2019 Spain Publisher:Elsevier BVJournal:Applied Soft Computing, volume 76, pages 356-368 (issn: 1568-4946,

Authors: L.G.B. Ruiz; M.I. Capel; M.C. Pegalajar;

doi: 10.1016/j.asoc.2018.12.028

handle: 10481/87119

Parallel memetic algorithm for training recurrent neural networks for the energy efficiency problem

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Abstract

Abstract In our state-of-the-art study, we improve neural network-based models for predicting energy consumption in buildings by parallelizing the CHC adaptive search algorithm. We compared the sequential implementation of the evolutionary algorithm with the new parallel version to obtain predictors and found that this new version of our software tool halved the execution time of the sequential version. New predictors based on various classes of neural networks have been developed and the obtained results support the validity of the proposed approaches with an average improvement of 75% of the average execution time in relation to previous sequential implementations.

Country

Spain

Related Organizations

University of Granada
Spain

Keywords

Manager–worker parallelization algorithms, Energy efficiency, Time series prediction, Evolutionary algorithms, Neural networks

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	30
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%