Chaotic PSO with Pitch Adjustment for Classification

In recent years, swarm intelligence algorithms have been successfully applied to solve clustering and classification problems. A chaotic PSO with pitch adjustment based classifier (PCPS-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 PSO. The particle swarm optimization (PSO) is a popular swarm algorithm, which has exhibited good performance on many optimization problems, but PSO suffers from the problem of early convergence into a local minima. To overcome the premature convergence and enhance the optimization performance, pitch adjustment operator from Harmony Search (HS) algorithm was introduced in this paper. The presence of pitch adjustment operator sharpens the convergence and tunes to the best solution. 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, Breast Cancer data classification and the Wine data classification. The experimental results show that the performance of the PCPS-classifier is comparable to or better than the k-nearest neighbor (k-NN) as a conventional classifier and Particle Swarm based classifiers (PS-classifier) and Genetic algorithm based classifiers (GA-classifier) as a new classifier

Chaotic PSO with Pitch Adjustment for Classification Keywords:

Decision hyperplanes , Particle Swarm Optimization , Pattern recognition