AN ADAPTIVE OPTIMIZATION ALGORITHM FOR ENHANCING COMMUNICATION THROUGHPUT IN REAL-TIME NETWORK SYSTEMS

Abstract

Modern communication networks face significant challenges in maintaining optimal throughput during dynamic operational conditions such as network congestion, bandwidth fluctuations, and unexpected system outages. This research presents an adaptive optimization algorithm designed to enhance communication speed and reliability in real-time network environments. The proposed algorithm dynamically adjusts transmission parameters based on network conditions, implements intelligent packet routing, and optimizes buffer management to minimize latency and packet loss. Through comprehensive simulation studies and real-world testing scenarios, the algorithm demonstrated a 34.7% improvement in average throughput compared to conventional static protocols. The system successfully maintained stable communication during network disruptions, with recovery times reduced by 42% during simulated outage events. Additionally, the algorithm exhibited remarkable adaptability to sudden traffic surges, maintaining quality of service even under peak load conditions. The findings suggest that adaptive optimization techniques can significantly enhance communication performance in dynamic network environments, making them suitable for applications in IoT systems, cloud computing platforms, and mobile communication networks. This research contributes to the development of more resilient and efficient communication infrastructure capable of responding intelligently to real-time network conditions.

DOI: 10.46121/pspc.45.1.1