A Multicast Routing Algorithm Using Multiobjective Optimization (original) (raw)
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A Genetic Algorithm Optimization for Multi-Objective Multicast Routing
Many applications require to send information from a source node to multiple destinations nodes. To support these applications, the paper presents a multi-objective based genetic algorithm, which is used in the construction of the multicast tree for data transmission in a computer network. The proposed algorithm simultaneously optimizes total weights (cost, delay, and hop) of the multicast tree. Experimental results prove that the proposed algorithm outper-forms a recently published Multi-objective Multicast Algorithm specially designed for solving the multicast routing problem. Also, the proposed approach has been applied to ten-node and twenty-node network to illustrate its efficiency. In addition, the execution time is reported for each studied case and the obtained results are compared with the results obtained by the previously based ant colony algorithm presented recently to solve the same problem. Finality, summing up the three objectives (cost, delay, and hop) to be one objective called the weight of the tree to speed up the searching process by using the proposed algorithm to find the best solutions.
In network communication and distributed systems, numerous applications transmit data from the source to several destinations. The multicast routing is a remarkable combinatorial optimization issue with multiple objectives to optimize. Hybrid multi-objective evolutionary algorithms are employed for multicast routing. In this study, Pareto optimality-based adaptive PSO with variants are projected. The proposed algorithm and its variants optimize the cost, delay, and lifetime of the multicast tree. The aim is to construct a multicast type tree for transmitting data to minimize the cost, delay, and lifetime. Various strategies such as multi-objective weighted sum, dynamic inertia weight, adaptive non-dominated sorting approaches, and multi-objective local search based on the Pareto hill-climbing approach are implemented to identify the shortest path. The results are analyzed with respect to the cost, delay, and lifetime. The results of the ANS_MOPSO_PHC algorithm overtake the other tec...
Multiobjective multicast routing algorithm for traffic engineering
Proceedings. 13th International Conference on Computer Communications and Networks (IEEE Cat. No.04EX969), 2004
This paper presents a new version of a multiobjective multicast routing algorithm (MMA) for traffic engineering, based on the Strength Pareto Evolutionary Algorithm (SPEA), which simultaneously optimizes the maximum link utilization, the cost of the tree, the maximum end-to-end delay and the average delay. In this way, a set of optimal solutions, known as Pareto set, is calculated in only one run, without a priori restrictions. Simulation results show that MMA is able to find Pareto optimal solutions. They also show that for dynamic multicast routing, where the traffic requests arrive one after another, MMA outperforms other known algorithms.
An Efficient Evolutionary Algorithm for Multicast Routing with Multiple Qos Constraints
Advances in Natural Computation, 2004
The bandwidth-delay-constrained least-cost multicast routing is a challenging problem in high-speed multimedia networks. Computing such a constrained Steiner tree is an NP-complete problem. In this paper, we propose a novel QoS-based multicast routing algorithm based on the genetic algorithms (GA). In the proposed method, the predecessors encoding is used for genotype representation. Some novel heuristic algorithms are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed GA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. This proposed algorithm has overcome all of the previous algorithms in the literatures.
Multi-objective optimization algorithm for multicast routing with traffic engineering
IEEE ICN, 2004
In this paper, we propose a multi-objective traffic engineering scheme using different distribution trees to multicast several flows. The aim is to combine into a single aggregated metric, the following weighting objectives: the maximum link utilization, the hop count, the total bandwidth consumption, and the total end-to-end delay. Moreover, our proposal solves the traffic split ratio for multiple trees. We formulate this multi-objective function as one with Non Linear programming with discontinuous derivatives (DNLP). Results obtained using SNOPT solver show that several weighting objectives are decreased and the maximum link utilization is minimized. The problem is NP-hard, therefore, a novel SPT algorithm is proposed for optimizing the different objectives. The behavior we get using this algorithm is similar to what we get with SNOPT solver. The proposed approach can be applied in MPLS networks by allowing the establishment of explicit routes in multicast events. The main contributions of this paper are the optimization model and the formulation of the multi-objective function; and that the algorithm proposed shows polynomial complexity.
Solving Multi-Objective Multicast Routing Problem Using a New Hybrid Approach
International Journal of Applied Evolutionary Computation, 2018
Multicastroutingistheproblemoffindingthespanningtreeofasetofdestinationswhoserootsare thesourcenodeanditsleavesarethesetofdestinationnodesbyoptimizingasetofqualityofservice parametersandsatisfyingasetoftransmissionconstraints.Thisarticleproposesanewhybridmulticast algorithmcalledHybridMulti-objectiveMulticastAlgorithm(HMMA)basedontheStrengthPareto EvolutionaryAlgorithm(SPEA)toevaluateandclassifythepopulationindominatedsolutionsand non-dominatedsolutions.DominatedsolutionsareevolvedbytheBatAlgorithm,andnon-dominated solutionsareevolvedbytheFireflyAlgorithm.Oldandweaksolutionsarereplacedbynewrandom solutionsbyaprocessofmutation.Thesimulationresultsdemonstratethattheproposedalgorithm isabletofindgoodParetooptimalsolutionscomparedtootheralgorithms.
MULTICAST ROUTING USING MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM USING WIRELESS MESH NETWORK
Multicasting is one of the most important applications in Wireless Mesh Networks. Strength Pareto Evolutionary Algorithm (SPEA) for routing problem is formulated as a multi-objective mathematical programming problem which attempts to minimize both cost and delay objectives simultaneously. Approach: The proposed approach handles the routing problem as a true multi-objective optimization problem with competing and non-commensurable objectives. Result: The simulation result clearly shows that SPEA stores non-dominated solutions externally in another continuously-updated population and uses a hierarchical clustering algorithm to provide the decision maker with a manageable pareto-optimal set. Conclusion: SPEA is applied to a 20 node network as well as to large size networks ranging from 50-200 nodes. The results demonstrate the capabilities of the proposed approach to generate true and well distributed pareto-optimal non-dominated solutions.
An Efficiency Based Genetic Approach for Multicast Best Effort Routing
New added value services delivered over a multiservices IP network require significant changes to its design, including the activation of multiple functions in all active components such as multicast routing, QoS capabilities, security mechanisms… The present article treats multicast routing algorithms and presents the new genetic algorithm BE-MARGAN that aims the construction of a diffusion tree ensuring the interconnection of all members with a minimal delay while optimising the global network bandwidth usage. The article compares also performances of BE-MARGAN with the well known and used routing algorithm RPF for fully evaluation of the scalability of the proposed algorithm.