Improved migration models of biogeography-based optimization for sonar dataset classification by using neural network

Abstract Classification of experimental datasets such as target and clutter in sonar applications is a complex and challenging problem. One of the most useful instrument to classify sonar datasets is Multi-Layer Perceptron Neural Network (MLP NN). In this paper, due to the optimally updating the weights and biases vector of the MLP NN, Biogeography-Based Optimization (BBO) is used to train the network. BBO has a fair ability to solve high-dimensional real-world problems (such as sonar dataset classification) by maintaining a suitable balance between exploration and exploitation phases. The performance of BBO is sensitive to the migration model, especially for high-dimensional problems. To improve the exploitation ability of BBO and to record the better results for classifying sonar dataset, we propose novel migration models such as exponential-logarithmic, and some improved migration models having different emigration and immigration mathematical functions. To validate the performance of the proposed classifiers, this network will classify three datasets with various sizes and complexities. The simulation results indicate that our newly proposed classifiers perform better than the other benchmark algorithms in addition to original BBO in terms of avoiding gets stuck in local minima, classification accuracy, and convergence speed.