Artificial neural network training using a new efficient optimization algorithm

Because search space in artificial neural networks (ANNs) is high dimensional and multimodal which is usually polluted by noises and missing data, the process of weight training is a complex continuous optimization problem. This paper deals with the application of a recently invented metaheuristic optimization algorithm, bird mating optimizer (BMO), for training feed-forward ANNs. BMO is a population-based search method which tries to imitate the mating ways of bird species for designing optimum searching techniques. In order to study the usefulness of the proposed algorithm, BMO is applied to weight training of ANNs for solving three real-world classification problems, namely, Iris flower, Wisconsin breast cancer, and Pima Indian diabetes. The performance of BMO is compared with those of the other classifiers. Simulation results indicate the superior capability of BMO to tackle the problem of ANN weight training. BMO is also applied to model fuel cell system which has been addressed as an open and demanding problem in electrical engineering. The promising results verify the potential of BMO algorithm.