Nurnajmin Qasrina Ann, . and Pebrianti, Dwi and Mohammad Fadhil, Abas and Bayuaji, Luhur (2023) Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system. International Journal of Electrical and Computer Engineering (IJECE), 13 (2). pp. 2167-2176. ISSN 2088-8708. (Published)
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Abstract
Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). AOA makes use of the distribution properties of mathematics’ primary arithmetic operators, including multiplication, division, addition, and subtraction. AOA is mathematically modeled and implemented to optimize processes across a broad range of search spaces. The performance of AOA is evaluated against 29 benchmark functions, and several real-world engineering design problems are to demonstrate AOA’s applicability. The hyper-parameter tuning framework consists of a set of Lorenz chaotic system datasets, hybrid DNN architecture, and AOA that works automatically. As a result, AOA produced the highest accuracy in the test dataset with a combination of optimized hyper-parameters for DNN architecture. The boxplot analysis also produced the ten AOA particles that are the most accurately chosen. Hence, AOA with ten particles had the smallest size of boxplot for all hyper-parameters, which concluded the best solution. In particular, the result for the proposed system is outperformed compared to the architecture tested with particle swarm optimization.
| Item Type: | Article |
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| Uncontrolled Keywords: | arithmetic optimization algorithm; automated-tuned system; deep neural network; lorenz chaotic system; optimization |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Noorul Farina Arifin |
| Date Deposited: | 07 Feb 2023 01:24 |
| Last Modified: | 07 Feb 2023 01:24 |
| URI: | http://umpir.ump.edu.my/id/eprint/36943 |
| Download Statistic: | View Download Statistics |
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