An optimal PSO distributed precoding algorithm in QRD-based multi-relay system

Zhang, Chaoyi; Muqing, Wu; Luan, Linlin

doi:10.1016/j.future.2012.04.003

Cited by 9 publications

(7 citation statements)

References 26 publications

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“…c 0 ; c 1 and c 2 are, respectively, the inertia factor, the cognitive (individual) and the social (group) learning rates. r swarm are, respectively, the best previously obtained position of the particle i and the best obtained position in the entire swarm at the current iteration ' where [15,16]:…”

Section: Pso Optimizationmentioning

confidence: 99%

Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

Bechouat

Younsi

Sedraoui

et al. 2017

Int J Energy Environ Eng

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Experimental studies confirm that the obtained electrical power by a conventional photovoltaic PV system is progressively degraded when the temperature of its cells is increased. The water-cooled photovoltaic thermal PVT system is therefore proposed to avoid the voltage drop at high temperature. The use of single diode PV/PVT models in simulation software becomes indispensable to analyze its performances where several climatic conditions such as environmental temperature and solar radiation variations should be considered. An optimal set of PV/PVT model parameters are determined through experimental data using two evolutionary computation algorithms; genetic algorithm and particle swarm optimization algorithm. Furthermore, the robustness of the given PV/PVT model should be analyzed. The predicted electrical properties by the proposed PVT model are compared with those given by the conventional PV model at its operating cell conditions and also at several rigid atmospheric conditions.

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Section: Pso Optimizationmentioning

confidence: 99%

Parameters identification of a photovoltaic module in a thermal system using meta-heuristic optimization methods

Bechouat

Younsi

Sedraoui

et al. 2017

Int J Energy Environ Eng

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“…Vanneschi et al [31] compared four parallel and distributed PSOs which inspires our work. Zhang et al [32] improved the PSO and deployed it into distributed spatial precoding. Sheng et al [33] used the standard PSO and map-reduce (MR) framework for prediction intervals (PIs) construction, which does not implement the parallelization of PSO.…”

Section: Decentralized Training Algorithmmentioning

confidence: 99%

Algorithm and Implementation of Distributed ESN Using Spark Framework and Parallel PSO

Wu¹,

Zhu²,

Li³

et al. 2017

Applied Sciences

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Abstract:The echo state network (ESN) employs a huge reservoir with sparsely and randomly connected internal nodes and only trains the output weights, which avoids the suboptimal problem, exploding and vanishing gradients, high complexity and other disadvantages faced by traditional recurrent neural network (RNN) training. In light of the outstanding adaption to nonlinear dynamical systems, ESN has been applied into a wide range of applications. However, in the era of Big Data, with an enormous amount of data being generated continuously every day, the data are often distributed and stored in real applications, and thus the centralized ESN training process is prone to being technologically unsuitable. In order to achieve the requirement of Big Data applications in the real world, in this study we propose an algorithm and its implementation for distributed ESN training. The mentioned algorithm is based on the parallel particle swarm optimization (P-PSO) technique and the implementation uses Spark, a famous large-scale data processing framework. Four extremely large-scale datasets, including artificial benchmarks, real-world data and image data, are adopted to verify our framework on a stretchable platform. Experimental results indicate that the proposed work is accurate in the era of Big Data, regarding speed, accuracy and generalization capabilities.

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“…Sun et al [10] proposed quantum-behaved particle swarm optimization (QPSO) as well as additional algorithms that improved QPSO. Although similar variants were continually devised [11][12][13][14], most of the improved PSO methods increased the complexity of the algorithm. This need not be the case, however, and Pedersen and Chipperfield [15] presented a simplified PSO called many optimizing liaisons (MOL) which is similar to "social-only" PSO: the only difference was that the search range would be decreased for all dimensions simultaneously by multiplying with a factor for each failure to improve the fitness.…”

Section: Previous Work On Particle Swarm Optimizationmentioning

confidence: 99%