A Novel Ant Colony Optimization Algorithm for Dynamic Routing in Communication Networks

This study on the evolving routing terrain of communication networks centers around improving ACO algorithms. A new method, Modified Ant Colony Optimization (MACO) is developed using dynamic pheromone updates according to current network states. The GA, PSO, and traditional ACO narrations on comparative analysis provide further evidence of MACO's superior adaptability of routing decisions in scenarios where the network conditions change quickly. We validate the proposed MACO algorithm in multiple folds using a typical dataset. Output results show a noticeable enhancement in network throughput, reaching extremely high values that are very low in terms of latency. Packet delivery ratios present better results that further point to the efficiency of MACO in providing successful information transfer. These results make MACO a strong candidate to solve the issues of unstable routing in communication networks.