Feasibility of Genetic Algorithms in 2D Ultrasound Array Optimization

The optimization of 2D ultrasound arrays can be efficiently realized through the use of simulated annealing (SA) as reported in many recent works. The main limitation of SA resides in the amount of resources required and the optimization time. In this work, the application of the genetic algorithms (GA) in 2D arrays optimization is presented to evaluate the potential of these algorithms compared to the simulated annealing in terms of processing time and output beam profiles. GA have been used in the optimization of multivariable cost functions in many applications as power systems analysis, wireless sensor arrays in telecommunication and they provided satisfactory results in a relative short time. The 2D ultrasound arrays optimization can also be formulated in a similar way as the problems occurring in these domains, making genetic algorithms a potential candidate to solve them. Although GA are reported to not be as efficient as SA in the optimization of medium and large 2D arrays, the aim of this paper is to evaluate their real ability when compared to the SA. Preliminary results showed a fast convergence time of genetic algorithm for comparable acoustical properties for the optimized arrays compared to SA.