استخدام ذكاء الأسراب لتحسين أداء الشبكات الخصومية التوالدية

Abstract

Generative Adversarial Networks (GANs) are a key technology in deep learning and artificial intelligence, forming an essential part of systems like ChatGPT. They generate new content—including images, audio, and text—by learning from existing data. Despite their capabilities, GANs face challenges, particularly due to the vast solution space, which makes finding optimal outputs time-consuming and hinders accurate generation of desired results. This is especially critical in chat applications, where precision and relevance are essential.This research proposes a model that integrates swarm intelligence algorithms with GANs to improve performance in chat systems. By guiding the search and reducing the number of potential solutions, the model enhances the accuracy and efficiency of output generation. Implementing this approach resulted in a 33% increase in accuracy compared to traditional GANs, demonstrating its effectiveness in producing more precise, targeted, and timely responses in conversational AI applications.