An improved arithmetic optimization algorithm for training feedforward neural networks under dynamic environments

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...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 263; p. 110274
Main Authors: Gölcük, İlker, Ozsoydan, Fehmi Burcin, Durmaz, Esra Duygu
Format: Journal Article
Language:English
Published: Elsevier B.V 05.03.2023
Subjects:
Arithmetic optimization algorithm
Artificial neural networks
Concept drift
Dynamic optimization
Arithmetic optimization algorithm
Concept drift
Artificial neural networks
Dynamic optimization
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary: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. •Artificial neural networks (ANNs) are trained in dynamic environments.•An improved arithmetic optimization algorithm (AOA) is developed.•The improved AOA is used to train ANNs under concept drift.•The effectiveness of the proposed algorithm is statistically verified.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2023.110274