ENHANCING ADAPTIVE LEARNING AND DECISION-MAKING SYSTEMS USING SWARM INTELLIGENCE AND DEEP LEARNING FOR ADVANCED AI APPLICATIONS

Abstract

The rapid development of autonomous vehicles (AVs) demands robust and adaptive AI systems capable of handling complex real-world environments. Traditional optimization and learning algorithms often struggle with dynamic and uncertain conditions, leading to suboptimal decision-making. Swarm intelligence, particularly Hawk Fire Optimization (HFO), offers a promising solution by simulating cooperative behaviors seen in nature, like hawks in hunting, to optimize decision-making processes. Coupled with advanced deep learning techniques like Federated Dropout Learning (FDL), this hybrid approach can enhance the adaptability, scalability, and efficiency of AI systems. This paper addresses the challenge of improving decisionmaking and learning in autonomous vehicles by integrating HFO with FDL. HFO optimizes parameters in real-time, allowing AVs to adapt rapidly to changing environments. Federated Dropout Learning, a variant of federated learning, further improves system resilience by sharing learning across distributed nodes while minimizing communication overhead and enhancing privacy. By combining these methods, the proposed system ensures robust performance in unpredictable scenarios. Experimental results show that the hybrid model outperforms traditional methods in terms of decision accuracy, response time, and energy efficiency. Specifically, the system achieved a 12% improvement in decision accuracy, reduced processing time by 18%, and cut energy consumption by 22%, compared to standard algorithms. These findings suggest that the combination of HFO and FDL can significantly improve the performance of autonomous vehicles, providing safer and more efficient AI-driven navigation.