Reliable Critical Infrastructure: Multiple Failures for Multicast using Multi-Objective Approach (original) (raw)
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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...
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This paper presents a new traffic engineering load balancing taxonomy, classifying several publications and including their objective functions, constraints and proposed heuristics. Using this classification, a novel Generalized Multiobjective Multitree model (GMM-model) is proposed. This model considers for the first time multitree-multicast load balancing with splitting in a multiobjective context, whose mathematical solution is a whole Pareto optimal set that can include several results than it has been possible to find in the publications surveyed. To solve the GMMmodel, a multi-objective evolutionary algorithm (MOEA) inspired by the Strength Pareto Evolutionary Algorithm (SPEA) is proposed. Experimental results considering up to 11 different objectives are presented for the well-known NSF network, with two simultaneous data flows.
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7th Catalan Conference …, 2004
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Multicastroutingistheproblemoffindingthespanningtreeofasetofdestinationswhoserootsare thesourcenodeanditsleavesarethesetofdestinationnodesbyoptimizingasetofqualityofservice parametersandsatisfyingasetoftransmissionconstraints.Thisarticleproposesanewhybridmulticast algorithmcalledHybridMulti-objectiveMulticastAlgorithm(HMMA)basedontheStrengthPareto EvolutionaryAlgorithm(SPEA)toevaluateandclassifythepopulationindominatedsolutionsand non-dominatedsolutions.DominatedsolutionsareevolvedbytheBatAlgorithm,andnon-dominated solutionsareevolvedbytheFireflyAlgorithm.Oldandweaksolutionsarereplacedbynewrandom solutionsbyaprocessofmutation.Thesimulationresultsdemonstratethattheproposedalgorithm isabletofindgoodParetooptimalsolutionscomparedtootheralgorithms.
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The increasing demand of real-time multimedia network within acceptable cost. Most of the research works applications in wireless environment requires stringent Quality focus on multicasting to a group for near optimal, fast solution of Service (QoS) provisioning to the Mobile Hosts (MI. The with multi-tree backup using GA .