PSOGSA: A parallel implementation model for data clustering using new hybrid swarm intelligence and improved machine learning technique

Abstract

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. 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 bio-inspired 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. Compared to the CPU-based regular PSO, the suggested Hybrid PSOGSA with NN achieved optimal clustering with 71% improved computational time.