Abstract
In today's data-driven world, the efficient management of network resources is
crucial for optimizing performance in data centers and large-scale networks. Load balancing is a
critical process in ensuring the equitable distribution of data across multiple paths, thereby
enhancing network throughput and minimizing latency. This paper presents a comprehensive
approach to load balancing using advanced optimization techniques integrated with multipath
routing protocols. The primary focus is on dynamically allocating network resources to manage
the massive volume of data generated by modern applications. By leveraging algorithms such
as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), the proposed method
efficiently distributes data across multiple paths, ensuring balanced network utilization. The
combination of these algorithms with multipath routing significantly reduces congestion and
improves overall network performance