scholarly journals Particle swarm optimization and gravitational wave data analysis: Performance on a binary inspiral testbed

2010 ◽  
Vol 81 (6) ◽  
Author(s):  
Yan Wang ◽  
Soumya D. Mohanty
Keyword(s):  
Particle Swarm Optimization ◽  
Data Analysis ◽  
Gravitational Wave ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wave Data

scholarly journals Butterfly-PSO Based Energy Efficient Data Analysis of Wireless IoT Devices

2019 ◽  
Vol 8 (3) ◽  
pp. 5779-5784
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Data Analysis ◽  
Model Comparison ◽  
Cluster Head ◽  
Back Propagation ◽  
Particle Swarm ◽  
Cluster Center ◽  
Swarm Optimization ◽  
Proposed Model

Paper collecting data from various sources for research observation, security, etc. are depend on IOT networks. As IOT device are remotely which transform information from nearby area and lifespan of this network rely on energy uses for communication. So this paper proposed a neural network and genetic algorithm combination for increasing the life span of the network. Error Back Propagation neural network was trained to identify best set of nodes for the cluster center selection. This machine learning based data selection increase the cluster selection accuracy of the BFPSO (Butterfly Particle Swarm Optimization). As combination get reduce by neural network data analysis so less number of population need to be developed for BFPSO algorithm which ultimately increase the accuracy of device selection. Various set of region size and number of nodes were developed to evaluate proposed model. Comparison of proposed model NN-BFPSO-CHS (Neural Network Butterfly Particle Swarm Optimization based Cluster Head Selection) was done with previous existing methods on different evaluation parameters and it was obtained that proposed model has improved all set of parameters

scholarly journals Parallel Computation Performingkernel-Based Clustering Algorithm Using Particle Swarm Optimization For The Big Data Analytics

2019 ◽  
Vol 8 (2) ◽  
pp. 4753-4756
Keyword(s):  
Big Data ◽  
Particle Swarm Optimization ◽  
Data Analysis ◽  
Parallel Computation ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Particle Swarm ◽  
Similarity Measures ◽  
Swarm Optimization ◽  
Non Linear

Digital data has been accelerating day by day with a bulk of dimensions. Analysis of such an immense quantity of data popularly termed as big data, which requires tremendous data analysis scalable techniques. Clustering is an appropriate tool for data analysis to observe hidden similar groups inside the data. Clustering distinct datasets involve both Linear Separable and Non-Linear Separable clustering algorithms by defining and measuring their inter-point similarities as well as non-linear similarity measures. Problem Statement: Yet there are many productive clustering algorithms to cluster linearly; they do not maintain quality clusters.Kernel-based algorithms make use of non-linear similarity measures to define similarity while forming clusters specifically with arbitrary shapes and frequencies. Existing System:Current Kernel-based clustering algorithms have few restraints concerning complexity, memory, and performance. Time and Memory will increase equally when the size of the dataset increase. It is challenging to elect kernel similarity function for different datasets. We have classical random sampling and low-rank matrix approximation linear clustering algorithms with high cluster quality and low memory essentials. Proposed work: in our research, we have introduced a parallel computation performing Kernel-based clustering algorithm using Particle Swarm Optimization approach. This methodology can cluster large datasets having maximum dimensional values accurately and overcomes the issues of high dimensional datasets.

scholarly journals Particle swarm optimization of the sensitivity of a cryogenic gravitational wave detector

2018 ◽  
Vol 97 (12) ◽  
Author(s):  
Yuta Michimura ◽  
Kentaro Komori ◽  
Atsushi Nishizawa ◽  
Hiroki Takeda ◽  
Koji Nagano ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Gravitational Wave ◽  
Particle Swarm ◽  
Gravitational Wave Detector ◽  
Swarm Optimization ◽  
Wave Detector
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