Particle Swarm Optimization for Creating Self Organized Evolutionary Back Propagation Artificial Neural Networks

Abstract

A novel technique for developing a self organized Back Propagation Artificial Neural Networks (BPANNs) or simply Back Propagation Neural Network (BPN) with the help of swarm Intelligence (SI) technique. This paper proposes an overlapping swarm intelligence algorithm for training of neural networks in which a particle swarm is assigned to each neuron to search for that neuron’s weights. The training of ANN is a difficult task when it’s get into the local minima and causes the low learning rates; it is required of an evolutionary multi dimensional algorithm to seek optimal weight values, the positional optimum values and the dimensional optimum in values in the dynamic problem space. The approach discussed throughout in this paper is the credit assignment process by first focusing on updating weights and biases swarms Intelligence and then evaluating the fitness of the particles using a network. With the proper adaptation of the SI-BPN process, the proposed method can develop an optimum network within an architecture space for a particular problem. Additionally, it provides a class list of all other potential configurations. This algorithm will provide superior learning ability to the traditional Back-Propagation (BP) method in terms of accuracy and speed.

Keywords: Swarms Intelligence, Artificial Neural Networks, Back Propagation Neural Network, Learning rates, Local minima, Problem space