The learning time of the synaptic weights for feedforward neural networks tend to be very long. In order to reduce the learning time, in this paper we propose a new learning algorithm for learning the synaptic weights of the single-hidden-layer feedforward neural networks by combining the upgraded bat algorithm with the extreme learning machine. The proposed approach can efficiently search for the optimal input weights as well as the hidden biases, leading to the reduced number of evaluations needed to train a neural network. The experimental results based on classification problems and comparison with other approaches from literature have shown that the proposed algorithm produces a satisfactory performance in almost all cases and that it can learn the weight factors much faster than the traditional learning algorithms.