ACO-Based Path Optimization Inspired by TSP for Routing Efficiency in Communication Networks

Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem, and it is generally concerned with searching for this shortest tour that passes through each of a collection of cities and returns to the point of departure. In recent years the properties of TSP have proved to be very transferable to network routing problems where the ability to select optimal paths is imperative for maintaining low congestion and efficient communication. In this paper, we have studied the application of the established swarm-based optimization algorithm, Ant Colony Optimization (ACO), for solving routing problems in communication networks under a basic TSP-inspired model. The research covers the critical issues such as network congestion, routing delay and slow convergence scenario that are often present in dynamic network scenario. In order to improve the performance of the ACO, the proposed algorithm combines candidate set to assess path quality and applies the mechanism of adaptive adjustment of parameters to enhance the search and accelerate the convergence. The approach is suitable for routing in various simulated network scenarios and is shown to be more efficient and stable.