Sonar Data Classification Using Neural Network Trained by Hybrid Dragonfly and Chimp Optimization Algorithms

Abstract

This paper proposes a hybrid Dragonfly Algorithm (DA) for training Multi-Layer Perceptron Neural Network (MLP NN) to design the classifier for solving complicated problems and distinguishing the real target from liars’ targets in sonar applications. Due to improve the cost computation and reducing the waste of time, a modified low-cost DA is designed for evaluation. To assess the accuracy of the technique, some well-known meta-heuristic trainers include Chimp Optimization Algorithm, Gravitational Search Algorithm (GSA), DA, and Particle Swarm Optimization (PSO) compared to show the accuracy of similar algorithms. DA and ChoA algorithms have remarkable features and a hybrid algorithm of them is proposed. The proposed classifier has acceptable performance, and two standard benchmark datasets are used to evaluate performance. The results show that the modified hybrid DA-ChoA has 15% less time-consuming and 4% better performance rather than the original dragonfly method.