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Adaptive habitat biogeography-based optimizer for optimizing deep CNN hyperparameters in image classification
Adaptive habitat biogeography-based optimizer for optimizing deep CNN hyperparameters in image classification
Jiayun Xin
(School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, 264209, Shandong, China; Corresponding author.)
Mohammad Khishe
(Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran; Center for Artificial Intelligence Applications, Yuan Ze University, Taiwan)
Diyar Qader Zeebaree
(Information Technology Department, Technical College of Duhok, Duhok Polytechnic University, Duhok, Iraq)
Laith Abualigah
(Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan; Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan; Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk, 71491, Saudi Arabia; MEU Research Unit, Middle East University, Amman, 11831, Jordan; Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon; School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, 27500, Malaysia)
Taher M. Ghazal
(Centre for Cyber Physical Systems, Computer Science Department, Khalifa University, United Arab Emirates; Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia; Applied Science Research Center, Applied Science Private University, Amman, 11937, Jordan)
Journal: Heliyon
•
• Elsevier
• ISSN: 2405-8440
Abstract
Deep Convolutional Neural Networks (DCNNs) have shown remarkable success in image classification tasks, but optimizing their hyperparameters can be challenging due to their complex structure. This paper develops the Adaptive Habitat Biogeography-Based Optimizer (AHBBO) for tuning the hyperparameters of DCNNs in image classification tasks. In complicated optimization problems, the BBO suffers from premature convergence and insufficient exploration. In this regard, an adaptable habitat is presented as a solution to these problems; it would permit variable habitat sizes and regulated mutation. Better optimization performance and a greater chance of finding high-quality solutions across a wide range of problem domains are the results of this modification's increased exploration and population diversity. AHBBO is tested on 53 benchmark optimization functions and demonstrates its effectiveness in improving initial stochastic solutions and converging faster to the optimum. Furthermore, DCNN-AHBBO is compared to 23 well-known image classifiers on nine challenging image classification problems and shows superior performance in reducing the error rate by up to 5.14%. Our proposed algorithm outperforms 13 benchmark classifiers in 87 out of 95 evaluations, providing a high-performance and reliable solution for optimizing DNNs in image classification tasks. This research contributes to the field of deep learning by proposing a new optimization algorithm that can improve the efficiency of deep neural networks in image classification.
Jiayun Xin, Mohammad Khishe, Diyar Qader Zeebaree, et al. (2024). "Adaptive habitat biogeography-based optimizer for optimizing deep CNN hyperparameters in image classification". Heliyon. https://doi.org/10.1016/j.heliyon.2024.e28147