Parameter Optimization of Support Vector Machine Using Dragonfly Algorithm

Support Vector Machine (SVM) parameters such as penalty and kernel parameters have a great influence on the complexity and accuracy of the classification model. In this paper, Dragonfly algorithm (DA) has been proposed to optimize the parameters of SVM; thus, the classification error can be decreased. To evaluate the proposed model (DA-SVM), the experiment adopted six standard datasets which are obtained from UCI machine learning data repository. For verification, the results of the DA-SVM algorithm are compared with two well-known optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The experimental results demonstrated that the proposed model is capable to find the optimal values of the SVM parameters and avoids the local optima problem.