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

被引：27
|
作者：
Ruiz, L. G. B. [1 ]
Capel, M. I. [2 ]
Pegalajar, M. C. [1 ]
机构：
[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada, Spain
[2] Univ Granada, Dept Software Engn, Granada, Spain
来源：
APPLIED SOFT COMPUTING | 2019年 / 76卷
关键词：
Energy efficiency; Neural networks; Time series prediction; Evolutionary algorithms; Manager-worker parallelization algorithms; DATA MINING TECHNIQUES; GENETIC ALGORITHM; SHORT-TERM; FAULT-DETECTION; MULTIOBJECTIVE OPTIMIZATION; PREDICTIVE CONTROL; CONSUMPTION; MODEL; METHODOLOGY;
D O I：
10.1016/j.asoc.2018.12.028
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：356 / 368
页数：13
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