A Hierarchical PSO Algorithm for Self-organizing Neural Network Design

Particle swarm optimization (PSO) algorithm has come to be widely used as a problem solving method in engineering and computer science. This algorithm is one of recently several highly desirable attributes, including the fact that the basic algorithm is very easy to understand and implement. It is similar in some ways to evolutionary algorithms, but requires less computational bookkeeping and generally fewer lines of code. In the traditional training algorithm, we will determine neuron for number of neural network, but we are unable to guarantee that is feasible to number. So, in this paper, a hierarchical particle swarm optimization (HPSO) is proposed , which can determine the structure of the neural network and tune the parameters in the neural network automatically. Experimental results have shown that the proposed HPSO has a good performance.