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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| Vydané v: | Knowledge-based systems Ročník 263; s. 110274 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
05.03.2023
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 110274 |
| Author | Ozsoydan, Fehmi Burcin Durmaz, Esra Duygu Gölcük, İlker |
| Author_xml | – sequence: 1 givenname: İlker orcidid: 0000-0002-8430-7952 surname: Gölcük fullname: Gölcük, İlker email: ilker.golcuk@bakircay.edu.tr organization: Department of Industrial Engineering, İzmir Bakırçay University, İzmir 35665, Türkiye – sequence: 2 givenname: Fehmi Burcin orcidid: 0000-0002-6368-4425 surname: Ozsoydan fullname: Ozsoydan, Fehmi Burcin email: burcin.ozsoydan@deu.edu.tr organization: Department of Industrial Engineering, Faculty of Engineering, Dokuz Eylül University, İzmir 35397, Türkiye – sequence: 3 givenname: Esra Duygu orcidid: 0000-0002-8882-333X surname: Durmaz fullname: Durmaz, Esra Duygu email: esradurmaz@gazi.edu.tr organization: Department of Industrial Engineering, Faculty of Engineering, Gazi University, Ankara 06570, Türkiye |
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| Keywords | Arithmetic optimization algorithm Concept drift Artificial neural networks Dynamic optimization |
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