Compact Neural Architecture Search for Image Classification Using Gravitational Search Algorithm

Document Type : Original Article

Authors

1 Computer Engineering, Software Engineering, Lorestan University, Khorramabad, Iran

2 Computer Engineering Department, Lorestan University, Khoram Abad

Abstract

This paper presents a compact neural architecture search method for image classification using the Gravitational Search Algorithm (GSA). Deep learning, through multi-layer computational models, enables automatic feature extraction from raw data at various levels of abstraction, playing a key role in complex tasks such as image classification. Neural Architecture Search (NAS), which automatically discovers new architectures for Convolutional Neural Networks (CNNs), faces challenges such as high computational complexity and costs. To address these issues, a GSA-based approach has been developed, employing a bi-level variable-length optimization technique to design both micro and macro architectures of CNNs. This approach, leveraging a compact search space and modified convolutional bottlenecks, demonstrates superior performance compared to state-of-the-art methods. Experimental results on CIFAR-10, CIFAR-100, and ImageNet datasets reveal that the proposed method achieves a classification accuracy of 98.48% with a search cost of 1.05 GPU days, outperforming existing algorithms in terms of accuracy, search efficiency, and architectural complexity.

Keywords

Main Subjects

References

‏
[1]     L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J. Big Data, vol. 8, no. 1, Mar. 2021.
[2]     H.-C. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285-1298, May 2016.
[3]     M. Hussain, J. J. Bird, and D. R. Faria, “A Study on CNN Transfer Learning for Image Classification,” in Advances in Computational Intelligence Systems, vol. 840, A. Lotfi, H. Bouchachia, A. Gegov, C. Langensiepen, and M. McGinnity, Eds. Cham: Springer International Publishing, 2019, pp. 191-202.
[4]     P. Ren et al., “A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions,” ACM Comput. Surv., vol. 54, no. 4, pp. 1-34, May 2022.
[5]     J. Huang, B. Xue, Y. Sun, M. Zhang, and G. G. Yen, “Particle Swarm Optimization for Compact Neural Architecture Search for Image Classification,” IEEE Trans. Evol. Comput., pp. 1-1, 2022.
[6]     E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: A Gravitational Search Algorithm,” Inf. Sci., vol. 179, no. 13, pp. 2232-2248, Jun. 2009.
[7]     S. Tabatabaei, “A new gravitational search optimization algorithm to solve single and multiobjective optimization problems,” J. Intell. Fuzzy Syst., vol. 26, no. 2, pp. 993-1006, 2014.
[8]     D. Pelusi, R. Mascella, and L. Tallini, “Revised Gravitational Search Algorithms Based on Evolutionary-Fuzzy Systems,” Algorithms, vol. 10, no. 2, p. 44, Apr. 2017.
[9]     E. Rashedi, E. Rashedi, and H. Nezamabadi-pour, “A comprehensive survey on gravitational search algorithm,” Swarm Evol. Comput., vol. 41, pp. 141-158, Aug. 2018.
[10]   “Spacetime and Geometry: An Introduction to General Relativity,” ResearchGate. [Online]. Available: https://www.researchgate.net/publication/230967918_Spacetime_and_Geometry_An_Introduction_to_General_Relativity. [Accessed: 24-Sep-2021].
[11]   O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211-252, Dec. 2015.
[12]   Y. Wang, J. Yan, Q. Sun, J. Li, and Z. Yang, “A MobileNets Convolutional Neural Network for GIS Partial Discharge Pattern Recognition in the Ubiquitous Power Internet of Things Context: Optimization, Comparison, and Application,” IEEE Access, vol. 7, pp. 150226-150236, 2019.
[13]   B. Koonce and B. Koonce, “MobileNetV3,” in Convolutional Neural Networks with Swift for Tensorflow, Berkeley, CA: Apress, 2021, pp. 125-144.
[14]   Z. Lu et al., “NSGA-Net: neural architecture search using multi-objective genetic algorithm,” 2019, pp. 419-427.
[15]   Z. Yue, B. Lin, Y. Zhang, and C. Liang, “Effective, Efficient and Robust Neural Architecture Search,” 2022, pp. 1-8.
[16]   S. Li, Y. Sun, G. G. Yen, and M. Zhang, “Automatic 
Applied and basic Machine intelligence research
Volume 2, Issue 1
September 2024
Pages 77-91

Files

Share

How to cite

Statistics