关键词: 蜜蜂群优化, 训练算法, 前馈神经网络

Abstract: Training an artificial neural network is an optimization task since it is desired to find optimal weight set of a neural network in training process.Traditional training algorithms have some drawbacks such as getting in local minima and computational complexity.This paper introduces a kind use for training artificial feed-forward neural network based on Bee Colony Algorithm.Bee Colony Optimization algorithm is a simple，robust and population based stochastic optimization algorithm.The algorithm combines the exploration and exploitation processes effectively，and adopts a certain search strategy to skip from local optimization.The algorithm is successfully applied to XOR，N-Bit Parity and Encoder-Decoder problems，compared with the BP algorithm.Simulation results show that the proposed algorithm has better performance than the traditional GD algorithm and the LM algorithm.

Key words: bee colony optimization, training algorithm, feed-forward neural network