Multicast Routing Using Genetic Algorithm Seen as a Permutation Problem
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 01
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A genetic algorithm for shortest path routing problem and the sizing of populations
IEEE Transactions on Evolutionary Computation
Preferred link based delay-constrained least-cost routing in wide area networks
Computer Communications
Multicast routing with end-to-end delay and delay variation constraints
IEEE Journal on Selected Areas in Communications
Robust shortest path problem based on a confidence interval in fuzzy bicriteria decision making
Information Sciences: an International Journal
An improved firefly algorithm for solving dynamic multidimensional knapsack problems
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.