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Multi-Criteria Weights on Ad Hoc Networks Using Particle Swarm Optimization for Optimal Path Pairs.

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Titel: Multi-Criteria Weights on Ad Hoc Networks Using Particle Swarm Optimization for Optimal Path Pairs.
Autoren: Gunantara, Nyoman, Sudiarta, Pande Ketut, Antara, I Nyoman Gde
Quelle: International Review of Electrical Engineering; Jan/Feb2018, Vol. 13 Issue 1, p15-22, 8p, 1 Diagram, 2 Charts, 4 Graphs
Schlagwörter: AD hoc computer networks, PARTICLE swarm optimization, PATH Pascal (Computer program language), SIGNAL-to-noise ratio, ENERGY consumption
Abstract: Optimal path pairs in ad hoc networks will cooperate and coordinate to establish cooperative communication. The number of hops and multi-criteria informing the path pairs determines the number of pathway pair solutions that occur. Multi-criteria reviewed are signal-tonoise ratio, load variance, and power consumption. Multi-criteria are made in scalarization with varying weights. The solution of the many pairs of the path and complicated computations in finding the optimal pair of the path used the particle swarm optimization method. In addition, it will be compared with the method of the genetic algorithm. From the simulation then the result is first, the optimum path pair with the rank-sum weight (RS) weight is the higher priority criterion given bigger weight and the result is optimal performance. Second, for power consumption, the path pairs choose a greater power consumption for the PSO method than the GA method. Third, the value of SNR with the PSO method is smaller than the GA method. Fourth, the value of variance load with the PSO method is greater than the GA method. Fifth, the PSO method takes less computation time than the GA method. [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
Beschreibung
Abstract:Optimal path pairs in ad hoc networks will cooperate and coordinate to establish cooperative communication. The number of hops and multi-criteria informing the path pairs determines the number of pathway pair solutions that occur. Multi-criteria reviewed are signal-tonoise ratio, load variance, and power consumption. Multi-criteria are made in scalarization with varying weights. The solution of the many pairs of the path and complicated computations in finding the optimal pair of the path used the particle swarm optimization method. In addition, it will be compared with the method of the genetic algorithm. From the simulation then the result is first, the optimum path pair with the rank-sum weight (RS) weight is the higher priority criterion given bigger weight and the result is optimal performance. Second, for power consumption, the path pairs choose a greater power consumption for the PSO method than the GA method. Third, the value of SNR with the PSO method is smaller than the GA method. Fourth, the value of variance load with the PSO method is greater than the GA method. Fifth, the PSO method takes less computation time than the GA method. [ABSTRACT FROM AUTHOR]
ISSN:18276660
DOI:10.15866/iree.v13i1.14082