Abstract
A method based on ant colony algorithm (ACA) is proposed to train weights and thresholds for Back-propagation (BP) neural network. BP algorithm has been widely used in training artificial neural network (ANN). This algorithm has many attractive features, such as adaptive learning, self-organism, and fault tolerant ability. All of them make BP one of the most successful algorithms in various fields. But, BP suffers from relatively slow convergence speed, extensive computations and possible divergence for certain conditions. As a new bionic algorithm, ACA has gained very good performance in solving traveling salesman problem (TSP) and other optimization problems. Its properties such as distributed computation, heuristic searching and robustness have well conquered the long convergence speed and premature problem, which are the main deficiencies of BP algorithm. Experiments suggest the method proposed has resolved those problems efficiently.
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