Digital data networks design using genetic algorithms

Communication networks have witnessed significant growth in the last decade due to the dramatic growth in the use of Internet. The reliability and service quality requirements of modern data communication networks and the large investments in communications infrastructure have made it critical to design optimized networks that meet the performance parameters. Digital Data Service (DDS) is a popular communication service that provides users with a digital connection. The design of a DDS network is a special case of the classic Steiner-tree problem of finding the minimum cost tree connecting a set of nodes, using Steiner nodes. Since it is a combinatorial optimization problem several heuristic algorithms have been developed including Tabu search, and branch and cut algorithm. In this paper, a new approach using genetic algorithms (GAs) is proposed to solve the problem. The results from GA are compared with the Tabu search method. The results indicate that GA performs as well as Tabu search in terms of solution quality but has lower computation time. However, reducing the number of iterations in Tabu search makes it faster than GA and comparable in solution quality with GA.