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Particle swarm optimization is used in several combinatorial optimizationproblems. In this work, particle swarms are used to solve quadratic programmingproblems with quadratic constraints. The approach of particle swarms is anexample for interior point methods in optimization as an iterative technique.This approach is novel and deals with classification problems without the useof a traditional classifier. Our method determines the optimal hyperplane orclassification boundary for a data set. In a binary classification problem, weconstrain each class as a cluster, which is enclosed by an ellipsoid. Theestimation of the optimal hyperplane between the two clusters is posed as aquadratically constrained quadratic problem. The optimization problem is solvedin distributed format using modified particle swarms. Our method has theadvantage of using the direction towards optimal solution rather than searchingthe entire feasible region. Our results on the Iris, Pima, Wine, and Thyroiddatasets show that the proposed method works better than a neural network andthe performance is close to that of SVM.

Quadratically constrained quadratic programming for classification using particle swarms and applications