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<p>This paper introduces a novel approach to enhancing the architecture of deep convolutional neural networks, addressing issues of self-design. The proposed strategy leverages the grey wolf optimizer and the multi-scale fractal chaotic map search scheme as fundamental components to enhance exploration and exploitation, thereby improving the classification task. Several experiments validate the method, demonstrating an impressive 87.37% accuracy across 95 random trials, outperforming 23 state-of-the-art classifiers in the study’s nine datasets. This work underscores the potential of chaotic/fractal and bio-inspired paradigms in advancing neural architecture.</p>

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Fractal and chaotic map-enhanced grey wolf optimization for robust fire detection in deep convolutional neural networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月3日

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  • Yassine Bouteraa,
  • Mohammad Khishe

摘要

This paper introduces a novel approach to enhancing the architecture of deep convolutional neural networks, addressing issues of self-design. The proposed strategy leverages the grey wolf optimizer and the multi-scale fractal chaotic map search scheme as fundamental components to enhance exploration and exploitation, thereby improving the classification task. Several experiments validate the method, demonstrating an impressive 87.37% accuracy across 95 random trials, outperforming 23 state-of-the-art classifiers in the study’s nine datasets. This work underscores the potential of chaotic/fractal and bio-inspired paradigms in advancing neural architecture.