Evaluating the Potential of Firefly Algorithm for SVM Optimization

Support Vector Machine (SVM) has emerged in recent years as a popular approach for classification of remote sensing data. SVMs don’t require huge training samples and have little possibility of over fitting however; the accuracy of SVM mainly depends on the parameters selection of it. So, one of the significant research problems in SVM is the selection of optimal parameters that can increase the accuracy of this classifier. Regularization constant C and kernel function parameters exert a considerable influence on the accuracy of SVM. In recent years, the development of parameter optimization for SVM is supported by evolutionary algorithms and bio-inspired metaheuristic algorithms such as swarm-based methods. This paper evaluates the potential of one of the swarm-based bio-inspired optimization methods called Firefly Algorithm (FA) for SVM optimization. FA is a metaheuristic algorithm, inspired by the flashing behavior of fireflies. The primary purpose for a firefly's flash is to act as a signal system to attract other fireflies. Firefly algorithm is