Comparative Analysis of Swarm Intelligence Techniques for Data Classification

This paper investigates the effectiveness of employing two relatively new swarm intelligence (SI) metaheuristic techniques in determining the accuracy of data classification problem. The SI metaheuristics analyzed are Grey wolf optimizer and firefly algorithm (FA). In this work, Grey wolf optimizer and firefly algorithm (FA) are used in training feed-forward neural networks (FNN) for the purpose of data classification. In experiments, the iris data has been used to evaluate the performance of the proposed algorithms. The experimental results obtained from these techniques are compared with that of a similar population-based technique, namely, particle swarm optimizer (PSO). Results obtained show that both GWO and Firefly algorithms provide superior solutions for the case studied.