  This paper presents the application of a newly developed nature-inspired
metaheuristic optimization method, namely the Adaptive Wind Driven Optimization
(AWDO), to the training of feedforward artificial neural networks (NN) and
presents a discussion into the future research of AWDO implementation in Deep
Learning (DL). Application example of digit classification with MNIST dataset
reveals interesting behavior of the derivative-free AWDO method compared to
steepest descent method where results and future work on the implementation of
AWDO in deep neural networks are discussed.
