A Novel Support Vector Machine Based on Hybrid Bat Algorithm and Its Application to Identification of Low Velocity Impact Areas

The standard support vector machine (SVM) performs poorly on the identification problem of low velocity impact areas due to its lower accuracy rate. Improving SVM's performance using the bat algorithm (BA) is feasible, but BA has the premature convergence problem. In this study, a hybrid bat algorithm with double mutation operations (DMBA), in which the Cauchy mutation operator and the extremal optimization mutation operator are integrated into BA, is proposed to enhance BA's ability to jump out of the local optima. Then, a novel SVM based on this hybrid BA, which is called SVM_DMBA, is developed to address the identification problem. Compared with the standard SVM and twelve improved SVM methods which are combined with the standard algorithms, advanced algorithms, and bat variants, the significant performance of SVM_DMBA is validated using UCI datasets. Moreover, to identify low velocity impact areas, SVM_DMBA is applied to the low velocity impact localization system based on fiber Bragg grating (FBG) sensors. The statistical results indicate that SVM_DMBA is a significantly effective method for identifying the low velocity impact areas and generates higher identification accuracy than comparative methods. For 64 low velocity impact areas of 30 mm × 30 mm on an aluminium plate, the average identification error obtained by SVM_DMBA is 1.615%.