Advertising Illustration Creation via Swarm Intelligence and Generative Adversarial Network

This paper proposes a novel framework that integrates swarm intelligence algorithms with controllable Generative Adversarial Networks (GANs) to meet the multi-objective demands of advertising illustration tasks. By combining Particle Swarm Optimization (PSO) for global parameter exploration with a StyleGAN-like architecture capable of fine-grained style manipulation, our method effectively balances visual fidelity, brand consistency, stylistic diversity, and computational efficiency. We formulate the generation process as a multi-objective optimization problem. Experiments conducted on a curated dataset show that the proposed method outperforms conventional GANs, conditional GANs, and evolutionary-based baselines. Ablation studies further demonstrate the importance of integrating both style and brand-related loss functions, while parameter sensitivity analyses highlight the role of swarm size, inertia weight, and acceleration coefficients in guiding the search toward optimal solutions.