A GPU-accelerated adaptation of the PSO algorithm for multi-objective optimization applied to artificial neural networks to predict energy consumption

Optimization research often confronts the challenge of developing time consuming processes. This article introduces an innovative approach that leverages the computational power of Graphics Processing Units (GPUs) to speed up that optimization process. We present an innovative adaptation of Particle...

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Veröffentlicht in:	Applied soft computing Jg. 160; S. 111711
Hauptverfasser:	Iruela, J.R.S., Ruiz, L.G.B., Criado-Ramón, D., Pegalajar, M.C., Capel, M.I.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.07.2024
Schlagworte:	Artificial neural networks Energy consumption prediction GPU/CUDA Model optimization Multi-objective algorithm Model optimization Energy consumption prediction Multi-objective algorithm GPU/CUDA Artificial neural networks
ISSN:	1568-4946, 1872-9681
Online-Zugang:	Volltext
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