學(xué)術(shù)空間 / 論文 / 期刊論文
Neural architecture search using an enhanced particle swarm optimization algorithm for industrial image classification
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| 作 者 | Cai, Rongna , Ouyang, Haibin *, Li, Steven , Wang, Gaige , Ding, Weiping |
| 期刊名稱 | Information Sciences |
| 狀 態(tài) | Elsevier BV, 2026 |
| 發(fā)表日期 | 2026 年 |
| 摘 要 |
To tackle challenges in industrial image defect detection, guided by three core hypotheses: dataset representativeness, continuous differentiable NAS search space, and GPU-based computing environment, this study presents an enhanced particle swarm optimization (PSO)-based neural architecture search (NAS) method designated as DNE-PSO-NAS. Firstly, it employs a two-level binary particle encoding scheme for network layer configurations and connectivity, transforming architecture search into a multi-dimensional optimization problem. Secondly, an improved MBConv module with CBAM is developed to reinforce the model’s ability to perceive local and global features of defects, thereby raising the signal-to-noise ratio for tiny defect regions. Additionally, dynamic ring neighborhood velocity topology and swarm entropy-driven mutation are proposed to balance exploration and exploitation, boosting PSO’s optimization efficiency. Finally, a low-fidelity evaluation strategy is incorporated, forming a three-stage framework that reduces input space via image downsampling, compresses convolutional layer parameters to lower spatial complexity, and adopts a dynamic training termination mechanism based on fitness tracking. Experiments on NEU-DET and WM-811?K datasets demonstrate that its discovered architectures surpass traditional CNNs and SOTA methods, with classification accuracy reaching 100% on NEU-DET and 93% on WM-811K. Meanwhile, our algorithm cuts computational costs significantly and the results highlight major benefits for real-time industrial quality inspection. |
| 訪問鏈接 | https://doi.org/10.1016/j.ins.2026.123141 |
| 附 件 |
