## 3 Method
### Overall Framework of ICSO

Table 1: Results of ICSO and state-of-the-art algorithms with different datasets on multiple mainstream architectures. ### Ablation Study and Analysis

**Comparison with some popular evolutionary algorithms:** In order to clearly demonstrate the advantages of ICSO, the experiment selected several well-recognized and widely applied algorithms in the field of optimization, including PSO [30], DE [31], CSO, BOA [32], and WOA [33], and conducted a systematic comparison between these algorithms and the proposed ICSO based on the WGAN-GP framework and CIFAR-10 dataset. In addition to the IS and FID, the experiment introduces density and coverage, which evaluate the quality and diversity of generated images from two perspectives: the degree of overlap between generated samples and real data in the feature space, and the coverage of categories. Through a comparison in Table 2, it can be clearly observed that ICSO exhibits significant superiority over other evolutionary algorithms in multiple key metrics. Further analysis of the reasons reveals that both DE and CSO rely on information exchange strategies among individuals to generate new offspring. However, relying solely on the intra-population evolution mechanism often fails to maintain population diversity, making the algorithm prone to falling into local optima. Both BOA and WOA utilize a two-stage mechanism involving global exploration followed by local exploitation to generate new candidate solutions. Nevertheless, due to the absence of mutation operators, these algorithms may suffer from limited population diversity, leading to premature convergence. In contrast, ICSO ingeniously incorporates the evolutionary mechanism of PSO, enabling the more precise and efficient tracking of the global optimal solution during the evolutionary iteration process, rather than simply performing crossover operations among individuals. From an overall efficiency perspective, ICSO demonstrates excellent performance. Although its two-stage operational mechanism leads to a longer runtime compared to PSO and DE, the performance improvement brought by this design far outweighs the additional computational cost. Compared with CSO, BOA, and WOA, ICSO also exhibits higher runtime efficiency. Unlike CSO, which relies solely on cross-dimensional crossover operations to escape local optima, and BOA and WOA, which employ a two-stage strategy combining global and local search, the strength of ICSO lies in its effective integration of a global best mechanism during the evolutionary process. This enables the algorithm to more accurately guide the population toward the global optimal solution, thereby enhancing both convergence speed and search quality. **Comparison with different objective functions:** By comparing multiple perspectives of the objective function in ICSO, the evolutionary process can be guided more precisely, thereby significantly improving the performance of GAN. We conduct similar experiments above, utilizing WGAN-GP on CIFAR-10, and apply the evaluation metrics of IS, FID, Density, and Coverage. It can be seen from Table 3 that ICSO has a competitive advantage in various metrics. Specifically, ICSO not only achieves higher scores on the IS, indicating that the images it generates are both realistic and diverse, but also achieves lower values on the FID, further demonstrating the high consistency between its generated samples and the real data distribution. In addition, ICSO also performs exceptionally well in

Figure 4: KDE plots of the target data and the generated data of various GANs trained on the synthetic dataset. terms of Density and Coverage, as it is able to generate dense sample points within the real data distribution while effectively covering a broader range of distribution areas, avoiding the problem of mode collapse. In summary, in the process of optimizing GAN, this algorithm comprehensively surpasses other comparative algorithms by enhancing the quality, diversity, and coverage breadth of the generated samples.