.png){kind=icon}
返回
An improved arithmetic optimization algorithm for training feedforward neural networks under dynamic environments
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2023.110274.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes an improved Arithmetic Optimization Algorithm (AOA) to train artificial neural networks (ANNs) under dynamic environments. Despite many successful applications of metaheuristic training of ANNs, these studies assume static environments, which might not be realistic in real-world nonstationary processes. In this study, the training of ANNs is modeled as a dynamic optimization problem, and the proposed AOA is used to optimize connection weights and biases of the ANN under the presence of concept drift. The proposed method is designed to work for classification tasks. The performance of the proposed algorithm has been tested on twelve dynamic classification problems. Comparative analysis with state-of-the-art metaheuristic optimization algorithms has been provided. The superiority of the compared algorithms has been verified using nonparametric statistical tests. The results show that the improved AOA outperforms compared algorithms in training ANNs under dynamic environments. The findings demonstrate the potential of improved AOA for dynamic data-driven applications.(c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Arithmetic optimization algorithm
Artificial neural networks
Concept drift
Dynamic optimization
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
机构
引用论文
分享收藏分享收藏
An efficient hybrid multilayer perceptron neural network with grasshopper optimization.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏分享收藏
查看更多内容