首页|Researchers from Egypt-Japan University of Science and Technology Describe Findings in Machine Learning (Psogsa: a Parallel Implementation Model for Data Clustering Using New Hybrid Swarm Intelligence and Improved Machine Learning Technique)
Researchers from Egypt-Japan University of Science and Technology Describe Findings in Machine Learning (Psogsa: a Parallel Implementation Model for Data Clustering Using New Hybrid Swarm Intelligence and Improved Machine Learning Technique)
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NETL
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Elsevier
A new study on Machine Learning is now available. According to news reporting originating from Alexandria, Egypt, by NewsRx correspondents, research stated, "With the digitization of the entire world and huge requirements of understanding unknown patterns from the data, clustering becomes an important research area. The quick and accurate division of large datasets with a range of properties or features becomes challenging." Our news editors obtained a quote from the research from the Egypt-Japan University of Science and Technology, "The parallel implementation of clustering algorithms must satisfy stringent computational requirements to handle large amounts of data. This can be achieved by designing a GPU based optimal computational model with a heuristic approach. Swarm Intelligence (SI), a family of bioinspired algorithms, that has been effectively applied to a number of real-world clustering problems. The Gravitational Search Algorithm (GSA) is a heuristic search optimization approach based on Newton's Law of Gravitation and mass interactions. Although it has a slow searching rate in the last iterations, this strategy has been proved to be capable of discovering the global optimum. This paper presents GPU based hybrid parallel algorithms for data clustering. A newly developed, hybrid Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) i.e., PSOGSA achieves the global optima. PSOGSA utilizes novel training methods for enhanced Neural Networks (NN) in order to examine the efficiency of algorithms and resolves the challenges of trapping in local minima. This also shows the sluggish convergence rate of standard evolutionary learning algorithms. The Nearest Neighbour Partition (Partitioning of the Neighbourhood) algorithm can be used to improve the performance of NN. A parallel version of Hybrid PSOGSA with NN is implemented to achieve optimal results with better computational time." According to the news editors, the research concluded: "Compared to the CPU-based regular PSO, the suggested Hybrid PSOGSA with NN achieved optimal clustering with 71% improved computational time." This research has been peer-reviewed.
AlexandriaEgyptAfricaAlgorithmsCyborgsData ClusteringEmerging TechnologiesMachine LearningSearch AlgorithmsSwarm IntelligenceEgypt-Japan University of Science and Technology
NETL
NSTL
Elsevier
