Open AccessHybrid ACO-PSO-GA-DE Algorithm for Big Data Classification
Author(s) -
Anju Bala
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1708.078219
Subject(s) - ant colony optimization algorithms , particle swarm optimization , differential evolution , computer science , meta optimization , hybrid algorithm (constraint satisfaction) , metaheuristic , convergence (economics) , genetic algorithm , mathematical optimization , support vector machine , algorithm , heuristic , artificial intelligence , mathematics , constraint satisfaction , probabilistic logic , economic growth , economics , constraint logic programming
This paper designs a technique to classify big data efficiently. This work considers the processing of big data as an optimization problem due to the trade-off between accuracy and time and solves this optimization problem by using a meta-heuristic approach. The HAPGD (Hybrid ACO (Ant Colony Optimization), PSO (Particle Swarm Optimization), GA (Genetic Algorithm), and DE (Differential Evolution)) classification algorithm is designed by using the support vector machine (SVM) along with hybrid ACO-PSO-GA-DE algorithm that hybrids exploration capability of ACO with exploitation capability of PSO whose balance is maintained using modified GA. The GA has been modified by using the DE algorithm. The presented technique performs classification efficiently as shown in results on seven datasets using different analysis parameters due to balanced exploration and exploitation search with fast convergence