## 3 Method
### Overall Framework of ICSO
### Industrial Application of StyleGAN3 with ICSO

To evaluate the performance and applicability of ICSO in industrial applications, we integrate it into the StyleGAN3 [34] network architecture, which possesses outstanding industrial characteristics, and apply it to the inspection of traditional electric poles, addressing challenges brought by grid expansion, such as increasing demand for inspection personnel and misjudgments or omissions due to extended inspection cycles. By applying ICSO to power line inspection, a typical industrial scenario, we not only validate its effectiveness in enhancing infrastructure resilience, but also further demonstrate its potential in automating and intelligentizing inspection tasks. At the same time, the generative AI capabilities embodied in this method provide traditional industrial systems with novel means of data generation and enhancement, offering technological support for advancing industrial digital transformation. Especially in the current context emphasizing green development, low carbon emissions, and efficient resource utilization, ICSO has the capability to generate large-scale, high-quality image data at relatively low real-world acquisition costs. This provides a feasible pathway toward building data reuse mechanisms, alleviating data acquisition bottlenecks, and achieving sustainable operations [35; 36]. By employing ICSO-GAN to generate images of electric poles taken by an unmanned aerial vehicle (UAV), the aim is to construct a dataset specifically for the automated inspection of electric poles. The experiment bases on the stylegan3-r-ffhuq-256x256 model as a pre-trained model with an additional 5000 kimgs of training. The original dataset contains a total of 1000 images of a single category of electric poles. To assess the performance of the proposed algorithm, it is compared with random Gaussian noise and I-FGSM optimized latent space in StyleGAN3. Evaluation metrics include fid50k_full (FID against the full dataset), kid50k_full (Kernel \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **IS (\(\uparrow\))** & **FID (\(\downarrow\))** & **Density (\(\uparrow\))** & **Coverage (\(\uparrow\))** & **GPU/Days** \\ \hline PSO & 7.577 & 23.036 & 0.478 & 0.726 & 2.0 \\ \hline DE & 7.583 & 22.164 & 0.474 & 0.729 & 2.2 \\ \hline CSO & 7.688 & 21.801 & 0.492 & 0.719 & 2.99 \\ \hline BOA & 7.655 & 21.912 & 0.485 & 0.735 & 3.02 \\ \hline WOA & 7.712 & 22.012 & 0.488 & 0.725 & 3.12 \\ \hline ICSO & **7.816** & **20.328** & **0.510** & **0.760** & 2.98 \\ \hline \hline \end{tabular} Note: \(\uparrow\) indicates that a higher value is better, while \(\downarrow\) indicates that a lower value is better.

\end{table} 
Table 2: Results of some classical evolutionary algorithms optimizing the latent space. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation** & **IS (\(\uparrow\))** & **FID (\(\downarrow\))** & **Density (\(\uparrow\))** & **Coverage (\(\uparrow\))** \\ \hline E-GAN & 7.652 & 21.624 & 0.492 & 0.747 \\ \hline IE-GAN & 7.693 & 21.574 & 0.506 & 0.750 \\ \hline Non-GP ICSO & 7.719 & 22.531 & 0.488 & 0.740 \\ \hline Unnormalized ICSO & 7.762 & 21.437 & 0.521 & 0.757 \\ \hline ICSO & **7.816** & **20.328** & **0.510** & **0.760** \\ \hline \hline \end{tabular} Note: \(\uparrow\) indicates that a higher value is better, while \(\downarrow\) indicates that a lower value is better.

\end{table} 
Table 3: Results of different objective functions of ICSO. inception distance [37] against the full dataset), pr50k3_full (Precision and recall [38] against the full dataset, i.e., pr50k3_full_precision and pr50k3_full_recall), pp12_wend (Perceptual path length [39] in latent space, endpoints, full image), eqt50k_int (Equivariance [34] w.r.t. integer translation: EQ-T), eqt50k_frac (Equivariance [34] w.r.t. fractional translation: EQ-Tfrac), and eqt50k (Equivariance [34] w.r.t. rotation: EQ-R). **Quantitative comparison:** As can be seen from Figure 5 and Table 4, ICSO demonstrates superior performance across multiple evaluation metrics.