Multicast Networking and Applications
Multicast Networking and Applications
An algebraic approach to network coding
IEEE/ACM Transactions on Networking (TON)
Network coding: an instant primer
ACM SIGCOMM Computer Communication Review
Multimedia Multicast on Internet
Multimedia Multicast on Internet
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Application of a Multi-objective Genetic Algorithm to Solve Reliability Optimization Problem
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 01
Introduction to Data Multicasting, IP Multicast Streaming for Audio and Video Media Distribution
Introduction to Data Multicasting, IP Multicast Streaming for Audio and Video Media Distribution
Introduction to Evolutionary Multiobjective Optimization
Multiobjective Optimization
Genetic Representations for Evolutionary Minimization of Network Coding Resources
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary multi-objective optimization in robot soccer system for education
IEEE Computational Intelligence Magazine
An Adaptive Multi-objective Image Watermarking Scheme for QIM Using NSGA-II
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 02
Information Sciences: an International Journal
Information Sciences: an International Journal
A population based incremental learning for delay constrained network coding resource minimization
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
Achieving balance between proximity and diversity in multi-objective evolutionary algorithm
Information Sciences: an International Journal
Entropy-based efficiency enhancement techniques for evolutionary algorithms
Information Sciences: an International Journal
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A distributed Cooperative coevolutionary algorithm for multiobjective optimization
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Information flow decomposition for network coding
IEEE Transactions on Information Theory
The encoding complexity of network coding
IEEE Transactions on Information Theory
Multicast performance with hierarchical cooperation
IEEE/ACM Transactions on Networking (TON)
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Network coding is a new communication technique that generalizes routing, where, instead of simply forwarding the packets they receive, intermediate nodes are allowed to recombine (code) together some of the data packets received from different incoming links if necessary. By doing so, the maximum information flow in a network can always be achieved. However, performing coding operations (i.e. recombining data packets) incur computational overhead and delay of data processing at the corresponding nodes. In this paper, we investigate the optimization of the network coding based multicast routing problem with respect to two widely considered objectives, i.e. the cost and the delay. In general, reducing cost can result into a cheaper multicast solution for network service providers, while decreasing delay improves the service quality for users. Hence we model the problem as a bi-objective optimization problem to minimize the total cost and the maximum transmission delay of a multicast. This bi-objective optimization problem has not been considered in the literature. We adapt the Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) for the new problem by introducing two adjustments. As there are many infeasible solutions in the search space, the first adjustment is an initialization scheme to generate a population of feasible and diversified solutions. These initial solutions help to guide the search towards the Pareto-optimal front. In addition, the original NSGA-II is very likely to produce a number of solutions with identical objective values at each generation, which may seriously deteriorate the level of diversity and the optimization performance. The second adjustment is an individual delegate scheme where, among those solutions with identical objective values, only one of them is retained in the population while the others are deleted. Experimental results reveal that each adopted adjustment contributes to the adaptation of NSGA-II for the problem concerned. Moreover, the adjusted NSGA-II outperforms a number of state-of-the-art multiobjective evolutionary algorithms with respect to the quality of the obtained nondominated solutions in the conducted experiments.