A New Method for Multimedia Multicast Routing in a Large Scale Network
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
A Genetic Algorithm for Steiner Tree Optimization with Multiple Constraints Using Prüfer Number
EurAsia-ICT '02 Proceedings of the First EurAsian Conference on Information and Communication Technology
Multicast routing with multiple constraints in high-speed networks based on genetic algorithms
ICCC '02 Proceedings of the 15th international conference on Computer communication
A novel algorithm for multimedia multicast routing in a large scale network
Journal of Systems and Software
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
Landscape analysis for multicast routing
Computer Communications
Guided local search as a network planning algorithm that incorporates uncertain traffic demands
Computer Networks: The International Journal of Computer and Telecommunications Networking
Solving the shortest path problem in vehicle navigation system by ant colony algorithm
ISCGAV'07 Proceedings of the 7th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
An Effective GA-Based Scheduling Algorithm for FlexRay Systems
IEICE - Transactions on Information and Systems
Swarm Intelligence Inspired Multicast Routing: An Ant Colony Optimization Approach
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Discrete Particle Swarm Optimization for Multiple Destination Routing Problems
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
A two-stage genetic algorithm for the multi-multicast routing
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
QoS-based MPLS multicast tree selection algorithms
Proceedings of the 7th International Conference on Frontiers of Information Technology
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
A hybrid integrated qos multicast routing algorithm in IP/DWDM optical internet
APPT'05 Proceedings of the 6th international conference on Advanced Parallel Processing Technologies
A hybrid genetic algorithm for solving the length-balanced two arc-disjoint shortest paths problem
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
GA-based heuristic algorithms for bandwidth-delay-constrained least-cost multicast routing
Computer Communications
Bandwidth-delay-constrained least-cost multicast routing based on heuristic genetic algorithm
Computer Communications
Optimizing virtual private network design using a new heuristic optimization method
ISRN Communications and Networking
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The multiple destination routing (MDR) problem can be formulated as finding a minimal cost tree which contains designated source and multiple destination nodes so that certain constraints in a given communication network are satisfied. This is a typical NP-hard problem, and therefore only heuristic algorithms are of practical value. As a first step, a new genetic algorithm is developed to solve the MDR problems without constraints. It is based on the transformation of the underlying network of an MDR problem into its distance complete form, a natural chromosome representation of a minimal spanning tree (an individual), and a completely new computation of the fitness of individual. Compared with the known genetic algorithms and heuristic algorithms for the same problem, the proposed algorithm has several advantages. First, it guarantees convergence to an optimal solution with probability one. Second, not only are the resultant solutions all feasible, the solution quality is also much higher than that obtained by the other methods (indeed, in almost every case in our simulations, the algorithm can find the optimal solution of the problem). Third, the algorithm is of low computational complexity, and this can be decreased dramatically as the number of destination nodes in the problem increases. The simulation studies for the sparse and dense networks all demonstrate that the proposed algorithm is highly robust and very efficient in the sense of yielding high-quality solutions