Search Results - (( parallel distribution issues algorithm ) OR ( parallel distribution swarm algorithm ))
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Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks
Published 2022“…Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN; (2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. …”
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Fruit-Fly Based Searching Algorithm For Cooperative Swarming Robotic System
Published 2013“…Therefore, the usage of a swarming robotic system is proposed. In this thesis, a simple framework and methodology in developing a bio-inspired algorithm for cooperative swarming robotic application has been developed. …”
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Thesis -
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Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing
Published 2013“…Although researches on the load management in the cloud systems is similar to that of traditional parallel and distributed systems in many aspects, essential differences exist between them. …”
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Parallel distributed genetic algorithm development based on microcontrollers framework
Published 2023“…This work is focused on the implementation of evolutionary based computer algorithms, genetic algorithms (GAs), on microcontrollers. …”
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The visualization of three dimensional brain tumors' growth on distributed parallel computer systems
Published 2009“…The main objective of this study is to visualize the brain tumors’ growth in three-dimensional and implement the algorithm on distributed parallel computer systems. …”
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Estimation of core size distribution of magnetic nanoparticles using high-Tc SQUID magnetometer and particle swarm optimizer-based inversion technique
Published 2023“…The combination of the high-Tc SQUID magnetometer and the PSO-based reconstruction technique offers a powerful approach for characterizing the MNP core size distribution, and further improvements can be expected from the recent state-of-the-art optimization algorithm to optimize further the computation time and the best objective function value.…”
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Topology-aware hypergraph based approach to optimize scheduling of parallel applications onto distributed parallel architectures
Published 2020“…The first step lies at the modelling of parallel applications running on heterogeneous parallel computers. …”
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Regenerative braking strategy for electric vehicles using improved adaptive genetic algorithm
Published 2017“…One uses Standard Genetic Algorithm (SGA), the second strategy uses Improved Adaptive Genetic Algorithm (IAGA). …”
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Adaptive resource allocation for reliable performance in heterogeneous distributed systems.
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Conjunctions in biological neural architectures for visual pose estimation / Tom´As Maul
Published 2006“…A highly parallel approach based on simple inter-map conjunctions, or correspondence distributions, is chosen and tested on synthetic and real patterns. …”
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An adaptive controller of hydro generators for smart grid application in Malaysia
Published 2010“…The controller has been modeled considering the existing Malaysia' distribution network of mini hydro power plant connected in parallel to the grid. …”
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A study on performance evaluation of conventional cache replacement algorithms: A review
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3d Morphing And Shape Transformation Using Slices
Published 2009“…This thesis addresses the above-mentioned issues by proposing a novel 3D morphing algorithm using slices. …”
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Critical insight for MAPReduce optimization in Hadoop
Published 2014“…Achieving an optimal approach for higher performance in terms of efficient load balancing, load distribution, optimum resource utilization, minimum overheads and least possible delay has been the vital issue for cloud infrastructure. …”
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Random sampling method of large-scale graph data classification
Published 2024“…After that, we randomly select a subset of data blocks, each being a random sample of the graph dataset, and compute the different graph property distributions. Since the data blocks in this model are much smaller than the entire data set, it is more efficient to analyze them on a standalone small machine, and multiple data blocks can be analyzed on multiple nodes of the cluster in parallel. …”
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