Classification of Sonar Targets Using an MLP Neural Network Trained by Dragonfly Algorithm

Due to the compatibility of the designed classifiers with MLP Neural Networks (MLP NNs), in this article, MLP NNs have been used to identify and classify active and passive sonar targets. On the one hand, the great importance of precise and immediate classification of sonar targets, and on the other hand, being trapped in local minimums and the low convergence speed in classic MLP NNs have led the newly proposed Dragonfly Algorithm (DA) to be offered for training MLP NNs. In order to assess the performance of the designed classifier, this algorithm have been compared with BBO, GWO, ALO, ACO, GSA and MVO algorithms in terms of precision of classification, convergence speed and the ability to avoid local optimum. To have a comprehensive comparison, the three sets of active and passive data were used. Simulation results indicate that DA-based classification have better results in all three datasets compared to benchmark algorithms.