Sparse Optimal Design of an Ultrasonic Sensor Array for Fast TFM Based on a Discrete Slime Mold Algorithm

The total focusing method (TFM) is an ultrasonic phased array imaging algorithm used in ultrasonic nondestructive testing (NDT) that processes large amounts of data from full matrix capture (FMC). This limits its application in some industrial fields with real-time requirements. To solve this problem, a sparse array optimization method is applied to FMC-TFM that can reduce time consumption and improve imaging efficiency. However, conventional intelligent optimization methods, such as genetic algorithm (GA), use binary encoding, which require intensive computation and are easily trapped in local optima. This article proposes a discrete slime mold algorithm (DSMA), in which the slime mold position is coded in real numbers instead of binary. In the optimization process, a mapping model between the slime mold and ultrasonic array is established. A fitness function with a narrow main lobe and low sidelobe is constructed to obtain the sparse array position with the best performance. In experiments, the proposed method reduces the imaging time by more than 50% compared with conventional TFM, without affecting imaging quality. Compared with a GA and binary particle swarm optimization (BPSO), the proposed method improves array performance indicator (API) and signal-to-noise ratio (SNR) performance.