Shaping an antenna radiation diagram with discrete weights using the PSO algorithm and periodic and nest spaces

Herrera, Oscar; Nuño, L.; Díaz‐Morcillo, Alejandro

doi:10.1016/j.mcm.2009.05.013

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…The number of executions means the number of independent experiments for calculating the average results. The inertia weight in the task-level strategy is 0.73 [32]. But at the workflow level, is set to 1.00 [15].…”

Section: Experimental Settingsmentioning

confidence: 99%

A Novel Workflow-Level Data Placement Strategy for Data-Sharing Scientific Cloud Workflows

Zhang

et al. 2019

IEEE Trans. Serv. Comput.

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Cloud computing can provide a more cost-effective way to deploy scientific workflows than traditional distributed computing environments such as cluster and grid. Due to the large size of scientific datasets, data placement plays an important role in scientific cloud workflow systems for improving system performance and reducing data transfer cost. Traditional tasklevel data placement strategy only considers shared datasets within individual workflows to reduce data transfer cost. However, it is obvious that task-level strategy is not necessarily good enough for the situation of multiple workflows at the workflow level. In this paper, a novel workflow-level data placement model is constructed, which regards multiple workflows as a whole. Then, a two-stage data placement strategy is proposed which first pre-allocates initial datasets to proper datacenters during workflow build-time stage, and then dynamically distributes newly generated datasets to appropriate datacenters during runtime stage. Both stages use an efficient discrete particle swarm optimization algorithm to place flexible-location datasets. Comprehensive experiments demonstrate that our workflow-level data placement strategy can be more cost-effective than its task-level counterpart for data-sharing scientific cloud workflows.

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Section: Experimental Settingsmentioning

confidence: 99%

A Novel Workflow-Level Data Placement Strategy for Data-Sharing Scientific Cloud Workflows

Zhang

et al. 2019

IEEE Trans. Serv. Comput.

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“…In addition, the boundary condition for PSO is also considered to be an influential factor due to the constraint of the allowable solution space [24], [25]. Shen et al discussed [26] restricted and unrestricted boundary conditions according to whether the particles are allowed to fly outside the allowable solution space or not.…”

Section: Related Workmentioning

confidence: 99%

Accelerated Coverage Optimization With Particle Swarm in the Quotient Space Characterizing Antenna Azimuths of Cellular Networks

et al. 2019

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In order to provide a reliable connection for the ever-increasing devices, cellular networks are facing critical challenges, among which is the issue of network coverage optimization. The costly objective function for network coverage appeals for an efficient approach of optimization, as the canonical particle swarm optimization (PSO) suffers excessive computation caused by population and iteration. The specificity of the antenna azimuths in cellular networks is hereby investigated so as to construct the corresponding metric structures in the naive antenna azimuth space and its quotient space. We introduce two accelerated PSO algorithms induced by the metric structures to maximize the cellular network coverage ratio. The former, named aPSO/S, only considers the shortest arc distance in the naive space, whereas the latter, named aPSO/QS, considers both the shortest arc distance and the equivalence among solutions in the quotient space. The proposed PSO algorithms, mainly the latter as referred to, not only guide the particles to fly toward the shortest arc distance directions but also eliminate lots of unnecessary calculation caused by the equivalence among solutions. The experiments show that the proposed aPSO/QS algorithm edge outs the canonical algorithm and its typical derivatives with various boundary conditions from the perspective of either the convergence speed or the stability with the hyper-parameters. To reach the same performance as that of its best competitor, the proposed aPSO/QS algorithm needs only a fraction of the computation cost so as to be applied in the efficiency-aware live network optimization.

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Optimization of linear antenna array for low SLL and high directivity

Saleem

Ahmed

2016

2016 19th International Multi-Topic Conference (INMIC)

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Shaping an antenna radiation diagram with discrete weights using the PSO algorithm and periodic and nest spaces

Cited by 3 publications

References 14 publications

A Novel Workflow-Level Data Placement Strategy for Data-Sharing Scientific Cloud Workflows

A Novel Workflow-Level Data Placement Strategy for Data-Sharing Scientific Cloud Workflows

Accelerated Coverage Optimization With Particle Swarm in the Quotient Space Characterizing Antenna Azimuths of Cellular Networks

Optimization of linear antenna array for low SLL and high directivity

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