Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
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
Solving shortest path problem using particle swarm optimization
Applied Soft Computing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on 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
A neural network for shortest path computation
IEEE Transactions on Neural Networks
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This paper presents an application of non-dominated sorting genetic algorithm-II (NSGA-II) technique for solving shortest path routing problems in computer networks. The problem is formulated as a non-linear constrained multi-objective optimisation problem. NSGA-II is applied to handle shortest path routing problem as a true multi-objective optimisation problem (MOOP) with competing and non-commensurable objectives. A priority-based encoding scheme is employed for population initialisation. Priorities are assigned to all the edges and NSGA-II is implemented to find the optimal solution. It is noted that this approach can find a diverse set of solutions and is converging near the true Pareto-optimal set. Results for a sample test network have been presented to demonstrate the capabilities of the NSGA-II algorithm to generate well-distributed Pareto-optimal solutions of shortest path routing problem in one single run. The results obtained by NSGA-II are compared with single objective weighting factor method for which genetic algorithm (GA) is applied.