Using Genetic Algorithms to Design Mesh Networks

• Authors: King-Tim Ko;Kit-Sang Tang;Cheung-Yau Chan;Kim-Fung Man;Sam Kwong
• Venue: Computer
• Year: 1997

Designing mesh communication networks is a complex, multiconstraint optimization problem. The design of a network connecting 10 Chinese cities demonstrates the elegance and simplicity that genetic algorithms offer in handling such problems. Designs for mesh communication networks must meet conflicting, interdependent requirements. This sets the stage for a complex problem with a solution that targets optimal topological connections, routing, and link capacity assignments. These assignments must minimize cost while satisfying traffic requirements and keeping network delays within permissible values. Since such a problem is NP-complete (one which has a solution in polynomial time, but can only be solved by nondeterministic algorithms), we must use heuristic techniques to handle the complexity and solve practical problems with a modest number of nodes. The heuristic methods used to design mesh networks include branch exchange, cut saturation, and Mentor algorithms. Another heuristic technique, genetic algorithms,1,2 appear ideal to design mesh networks with capability of handling discrete values, multiobjective functions, and multiconstraint problems.3 Existing applications of genetic algorithms to this problem,4-6 however, have only optimized the network topology. They ignore the difficult subproblems of routing and capacity assignment, a crucial determiner of network quality and cost. We present a total solution to mesh network design using a genetic algorithm approach. Not only does our method optimize network topology, it also optimizes routing and capacity assignment. In the following design for a proposed communications network, genetic algorithms produced a solution that costs 9 percent less and has two-thirds the delay of a typical design method. In our method, each optimization level uses genetic algorithms as its core, a similarity that reduces the complexity of system design. The advantages of this approach are not only its elegance and algorithmic simplicity, but also its ability to handle complicated issues such as continuous and discrete link capacities, linear or discrete cost structures, additional constraints, and various constraint models. We believe our genetic-algorithm approach to network design is novel and better than existing methods. Our 10-city network demonstrates that this method can be used for networks of reasonable size with realistic topology and traffic requirement.