Sparse TFM imaging by artificial bee colony-genetic algorithm

Sparse array design is an effective way to improve the real-time performance of ultrasonic phased array imaging. Genetic algorithm can solve the typical constrained optimization problem of linear array sparsity well. However, the local search ability of genetic algorithm is poor, and the search efficiency is low in the late evolution period. Therefore, in this paper, the process of finding the optimal solution of bees is introduced into the traditional genetic algorithm by constructing the array sparse method of artificial bee colony-genetic (ABCG) algorithm to increase the searching ability of global optimal solution. Simulation results show that the peak sidelobe level (PSL) of the sparse array optimized by ABCG algorithm reaches -11.40 dB, which shows higher sidelobes suppression ability than the sparse array optimized by genetic algorithm. The main lobe width (MLW) optimized by the two algorithms is basically equivalent to 2.8°, the main lobe width of full array at the threshold of -6 dB. Finally, a phased array detection system is used to collect ultrasonic signals from rail samples, and total focus method (TFM) is performed with the sparse matrix designed by ABCG algorithm. Experimental results show that when the sparsity rate of 32-element linear array reaches 75%, the array performance indicator (API) and signal to noise ratio (SNR) of the sparse array are not much different from that of full array, but the imaging efficiency is improved by 53.04%.