Enhancing router efficiency with intelligent shortest path algorithms

Modern communication networks are getting more complicated and changing all the time, so they need routing solutions that aren't just static formulas. The main goal of this study is to improve router performance by creating smart shortest path algorithms that can change with the network conditions in real time. The proposed hybrid models try to reduce latency, make the best use of bandwidth, and boost overall Quality of Service (QoS) by combining traditional routing methods with AI methods like Ant Colony Optimisation (ACO), Reinforcement Learning (RL), and Genetic Algorithms (GA). Simulations are used to test the new methods in a variety of topologies, such as mesh, ring, and random graphs, with varying amounts of traffic and link conditions. Standard protocols like Dijkstra, OSPF, and AODV are looked at and compared in terms of performance measures such as average delay, packet delivery ratio, convergence time, and routing overhead. Our results show that the suggested models can be used to solve problems in next-generation networks like IoT, SDN, and 6G infrastructures because they make routing more efficient and flexible