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This paper discusses the application of a multi-layer perceptron network to estimate direction of arrival (DOA) using ant colony optimization (ACO) for training. ACO simulates the foraging behavior of ant colonies which manage to find the shortest path from nest to feeding source. This technique was originally developed for discrete optimization problems, but recent research efforts has led to some algorithm modifications to make it applicable to continuous optimization problems. In this work we utilize continuous ACO to train a neural network for direction of arrival estimation which encounters an interpolation of a complex nonlinear function. The performance of proposed hybrid approach is compared to radial basis function network that is a well known solution to DOA problem and some improvements in approximation are discussed.