A Multi-Swarm ABC Algorithm for Parameters Optimization of SOFM Neural Network in Dynamic Environment

Abstract

Self-organizing feature map (SOFM) neural network is a kind of competitive unsupervised learning neural network, which has strong self-organizing and self-learning capabilities. It has been widely used in the fields of data classification and data clustering. A crucial step for SOFM neural network is to set its weight parameters correctly because the output accuracy and efficiency of the network depend much on these parameters. Most of current methods for parameter setting are based on static data. However, in a dynamic environment, the statistical characteristics of the generated data will change unpredictably over time. If the SOFM network cannot react to the changes of the environment, its performance will degrade. To deal with this problem, a more powerful multi-swarm artificial bee colony algorithm (MABC) is proposed. In the algorithm, the classic ABC algorithm is improved with multi-swarm and exclusive operation strategies to make it suitable for tracking optimal parameter settings of the SOFM network, so that the SOFM network can be applied to a dynamic environment. Two real data streams, which are regarded as coming from dynamic environments, are used to evaluate the effectiveness of the algorithm. Results show that the proposed algorithm is superior to the classic SOFM algorithm in terms of clustering purity and effectiveness. It is a promising method for the classification of data streams from dynamic environments.