Designing Quality of Service Solutions for the Enterprise
Designing Quality of Service Solutions for the Enterprise
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
A Multiobjective Model for QoS Multicast Routing Based on Genetic Algorithm
ICCNMC '03 Proceedings of the 2003 International Conference on Computer Networks and Mobile Computing
A multitree approach for multicast routing
LANC '05 Proceedings of the 3rd international IFIP/ACM Latin American conference on Networking
Multi-objective optimization scheme for multicast flows: a survey, a model and a MOEA solution
LANC '05 Proceedings of the 3rd international IFIP/ACM Latin American conference on Networking
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Research: Source-based delay-bounded multicasting in multimedia networks
Computer Communications
QoS routing based on genetic algorithm
Computer Communications
Bandwidth-delay-constrained least-cost multicast routing based on heuristic genetic algorithm
Computer Communications
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Multicasting routing is an effective way to communicate among multiple hosts in computer networks. Usually multiple quality of service (QoS) guarantees are required in most of multicast applications. Several researchers have investigated genetic algorithms-based models for multicast route computation with QoS requirements. The evolutionary models proposed here use multi-objective approaches in a Pareto sense to solve this problem and to deal with the inheriting multiple metrics involved in QoS proposal. Basically, we construct three QoS-constrained multicasting routing algorithms; the first one was based on NSGA, the second one was based on NSGA-II and the third is an adaptation of NSGA-II incorporating the concept of Ɛ-dominance. These algorithms were applied to find multicast routes over two network topologies. Three different pairs of objectives were evaluated; the first objective used in each pair is related to the total cost of a multicast route and the second metric is related to delay. The first evaluated delay metric computes the total delay involved in the tree solution; the second one computes the mean delay accumulated from the source to each destination node; the third one is the maximum delay accumulated from the source to a destination node. Our results indicated that the NSGA-II environment incorporating the concept of Ɛ-dominance - named Ɛ-NSGA-II multicasting routing - returned the best performance.