A nondominated sorting genetic algorithm solution for shortest path routing problem in computer networks

The shortest path routing problem is a multiobjective nonlinear optimization problem with a set of constraints. This problem has been addressed by considering delay and cost objectives simultaneously and as a weighted sum of both objectives for comparison. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the nondominated sorting genetic algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the succeeding generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which genetic algorithm (GA) is applied.