Abstract:

Abstract Artificial Neural Networks are a method frequently used in problem-solving today. In the past, it has been used in many areas such as classification, pattern recognition and image processing. The most important and demanding part of Artificial Neural Networks is the training process of the network. The main challenge in network training is the process of determining the optimum connection weights and bias values for the network. In the literature, many algorithms have been proposed for training Artificial Neural Networks. This article proposed a new hybrid algorithm called CSA-MLP for training Artificial Neural Networks using the Crow Search Algorithm. Crow Search Algorithm is a population-based meta-heuristic optimization algorithm, inspired by the behavior of crows to store their surplus nutrients and take them back from the storage area when needed. Crow Search Algorithm has been proposed to solve different optimization problems in terms of its simplicity with two different adjustable parameters (flight length and awareness probability), obtaining an effective convergence rate in a short time and having a faster technique compared to algorithms frequently used in engineering problems with different constraints and functions. In the experiments, five classification datasets (xor, balloon, iris, breast cancer, heart) were used. The CSA-MLP algorithm was compared with the SMS-MLP in terms of the mean squared error, classification rate, the statistical metrics (sensitivity, specificity, precision, f1-score) and the convergence graph. Furthermore, the proposed CSA-MLP algorithm was compared with seven algorithms in literature in terms of best classification accuracy. The experimental results show that the Crow Search Algorithm is a reliable approach in training Multi-Layer Perceptron. CSA-MLP achieved better results than SMS-MLP and other algorithms.

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