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

Qi Liu,Fengde Wang,Lei Wu,Wensheng Xiao

doi:10.1109/access.2019.2963163

Abstract

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 $30\,\,\text {mm}\times 30\,\,\text {mm}$ on an aluminium plate, the average identification error obtained by SVM_DMBA is 1.615%.

Highlights

Low velocity impact damages often occur on the ship because of accidents which include collisions with floating ice, stranding, and explosion
The frequency characteristics of impact signals received by fiber Bragg grating (FBG) sensors were used as the input of support vector machine (SVM), and the categories of low velocity impact areas were used as the output of SVM
The Cauchy mutation operator with the large mutation size focus on extending the search space of the bat during the early iterations, whereas the extremal optimization mutation operator with the small mutation size focus on searching the best solutions during the later iterations

Summary

Introduction

Low velocity impact damages often occur on the ship because of accidents which include collisions with floating ice, stranding, and explosion. The identification of low velocity impact areas on the ship is important. The location of impact source can be identified according to impact signals which are acquired from the sensing system in the structure. Sensor types in the sensing system for structural health monitoring (SHM) include piezoelectric sensors (PZT), fiber optic sensors, and other types of sensors. Ships always sail in the harsh marine environment, which requires that sensors have better performance. Compared with the electrical sensor, the fiber Bragg grating (FBG) sensor is

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