A novel bat algorithm with double mutation operators and its application to low-velocity impact localization problem

The low-velocity impact localization in the plate structure of the ship is a critical problem which can be considered as a nonlinear optimization problem. The bat algorithm (BA) has been widely used to solve nonlinear optimization problems. However, the standard BA exhibits poor performance on complex problems because of its premature convergence. In this study, a novel bat algorithm with double mutation operators (TMBA), in which a modified time factor and two mutation operators are integrated, is proposed to enhance BA's performance on nonlinear optimization problems. Classical benchmark functions are employed to analyze the contributions of the three modifications and demonstrate the significant improvement of TMBA. For the low-velocity impact localization problem, the low-velocity impact localization system based on fiber Bragg grating (FBG) sensors is utilized to receive the impact signals. The wavelet threshold de-noising method and the generalized cross-correlation method are both applied to the extraction of time differences between the impact signals. Then, the proposed algorithm and several well-known optimization algorithms are adopted to solve the minimization fitness function which is established using the triangulation method. The statistical results indicate that TMBA is more feasible and effective for solving the low-velocity impact localization problem.