Genetic Fuzzy Logic Algorithm as Intelligent Agents for Swarm Intelligence Application

This research introduces a novel approach to harnessing the power of Genetic Fuzzy Logic Algorithms (GFLAs) in the context of Swarm Intelligence applications. Swarm Intelligence relies on decentralized, self-organized systems, where individual agents collaborate to achieve complex tasks. However, existing methods often face challenges in adapting to dynamic environments and optimizing system performance. To address this, our study proposes the integration of GFLAs as intelligent agents within Swarm Intelligence frameworks. GFLAs leverage genetic algorithms and fuzzy logic to evolve and adapt their rules autonomously, enhancing the system adaptability and efficiency. The research addresses the gap in current literature by investigating the potential of GFLAs as intelligent agents in Swarm Intelligence, emphasizing their ability to learn and optimize behaviors in real-time. Through rigorous experimentation, we demonstrate the effectiveness of the proposed method in improving swarm performance across diverse scenarios.