Abstract—An Intelligent Particle Swarm classifier (IPS-

classifier) is proposed in this paper. This classifier is described for finding the decision hyperplanes to classify patterns of different classes in the feature space using particle swarm optimization (PSO) algorithm. An intelligent fuzzy controller is designed to improve the performance and efficiency of proposed swarm intelligence based classifier by adapting three important parameters of PSO (i.e., swarm size, neighborhood size, and constriction coefficient). Three pattern recognition problems with different feature vector dimensions were used to demonstrate the effectiveness of the proposed classifier. They are the Iris data classification, the Wine data classification, and radar targets classification from backscattered signals. The experimental results show that the performance of the IPS-classifier is comparable to or better than the k-nearest neighbor (k-NN) and multi-layer perceptron (MLP) classifiers, which are two conventional classifiers. Index Terms—Decision hyperplanes, fuzzy controller, particle swarm optimization, pattern recognition. I