Design of an optimal communication network using multiobjective genetic optimization

Designing an optimal network requires careful optimization of conflicting requirements. It is an NP hard problem. Traditional approaches to this problem have been based either on heuristics or on rigorous mathematical programming, queuing theory and network flow concepts. In this work, the authors describe the use of the multi-objective genetic optimization technique to obtain a Pareto front-a set of solutions which are optimal with respect to a set of constraints and noninferior to each other-for the network design problem. A prototype is developed and the simulator is currently being tested on different sets of inputs.