## 3 Method
### Overall Framework of ICSO
Specifically, ICSO achieves the best performance on metrics such as fid50k_full, kid50k_full, pr50k3_full_precision, eqt50k_int, and eqt50k. These metrics cover various aspects of generative models, including image quality (such as FID and KID), precision, and recall. Although ICSO's performance is slightly inferior to other methods on some metrics (such as pr50k3_full_recall, pp12_wend and eqt50k_frac), overall, ICSO shows competitiveness across most metrics. This indicates the robustness and effectiveness of ICSO in handling complex tasks. **Qualitative results:** StyleGAN3 generates random images from a latent space that has been optimized using various algorithms. As shown in Figure 6, the images generated by other algorithms are distorted when compared with those generated by ICSO. Since ICSO \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Gaussian Noise** & **I-FGSM** & **ICSO** \\ \hline fid50k\_full (\(\downarrow\)) & 60.591 & 56.371 & **52.639** \\ \hline kid50k\_full (\(\downarrow\)) & 0.024 & 0.022 & **0.019** \\ \hline pr50k3\_full\_precision (\(\uparrow\)) & 0.445 & 0.433 & **0.451** \\ \hline pr50k3\_full\_recall (\(\uparrow\)) & 0.151 & **0.201** & 0.133 \\ \hline pp12\_wend (\(\uparrow\)) & 2517.458 & **2545.676** & 2254.295 \\ \hline eqt50k\_int (\(\downarrow\)) & 57.132 & 56.699 & **56.689** \\ \hline eqt50k\_frac\(\downarrow\)) & **41.085** & 41.324 & 41.317 \\ \hline eqt50k (\(\downarrow\)) & 35.850 & 35.955 & **35.707** \\ \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 4: Minimum values of ICSO and other algorithms on various metrics. Figure 5: Comparison of ICSO with other algorithms across various metrics. incorporates the global optimal mechanism and the local optimal mechanism from PSO on the basis of the original CSO algorithm, it can always guide the evolution of the population towards the globally optimal direction, rather than relying solely on heuristic iterations. ## 5 Conclusions

This paper proposes the improved crisscross optimization (ICSO) algorithm for optimizing the latent space of GAN. In the algorithm, normalization is employed to equilibrate the order of magnitude between two independent attributes of quality and diversity within the objective function. Then, the gradient of the discriminator is constrained by introducing the gradient regularization term (i.e., GP) into the discriminator's loss function, which helps to avoid problems such as mode collapse and gradient exploding in the training process of GAN. By incorporating the local and global search mechanisms of PSO with the rapid convergence of the original crisscross optimization, ICSO effectively directs the evolution of population towards the optimal individuals. The experiments show that the proposed algorithm has significant advantages in the quality and diversity of the generated images, and it also has the prospect of being applied to industrial applications, specifically in the area of UAV image generation. In the future, we will study how to leverage attention mechanisms, wavelet analysis, and other technologies to explore the impact of latent space on the generation performance of GAN in greater depth. Methodology, Z.C. ([PERSON]), T.L. and Z.C. ([PERSON]); Software, Z.C. ([PERSON]) and Z.L. ; Validation, Z.C. ([PERSON]), Z.C. ([PERSON]), Z.L. and R.C. ; Data curation, Z.C. ([PERSON]); Writing--original draft, Z.C. ([PERSON]); Writing--review & editing, T.L., Z.C. ([PERSON]), Z.L. and R.C. ; Visualization, Z.C. ([PERSON]) and T.L. ; Supervision, Z.C. ([PERSON]); Project administration, T.L. ; Funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.