Abstract

Efficient traffic management in wireless networks is crucial for optimizing resource utilization and enhancing overall network performance. This paper introduces a novel approach to dynamic routing algorithms utilizing evolutionary algorithms for effective wireless traffic management. The proposed system leverages the adaptability and optimization capabilities of evolutionary algorithms to dynamically adjust routing paths based on real-time network conditions. Our algorithm employs a genetic programming framework to evolve and refine routing strategies, considering factors such as network congestion, link quality, and traffic load. This dynamic approach enables the network to autonomously adapt to changing conditions, ensuring optimal route selection for data transmission. The evolutionary nature of the algorithm allows it to continually learn and improve, making it well-suited for the dynamic and unpredictable nature of wireless environments. The effectiveness of the proposed algorithm is evaluated through extensive simulations, demonstrating significant improvements in terms of throughput, latency, and overall network efficiency compared to traditional static routing approaches. The system ability to handle diverse traffic patterns and adapt to varying network scenarios positions it as a robust solution for next-generation wireless networks.