Optimization of elastic wave propagation in a reconfigurable medium by genetic algorithms with adaptive mutation probability

Abstract

We introduce a reconfigurable medium for the manipulation of elastic propagation properties of Lamb waves. It is based on a shape memory polymer (SMP) with temperature-dependent Young’s modulus. Waves are excited by a laser pulse and detected by a laser vibrometer. A two-dimensional temperature field is controlled by a scanning heating laser. We use genetic algorithms to determine optimal distributions of mechanical properties for the following criteria: the wave amplitude has to be maximized at a given location and at the same time minimized at one or two other locations. Due to the reconfigurability of the medium, the optimization process is performed directly on the object of optimization, and not on a numerical or analytical representative, based on a direct measurement of the fitness. The optimized configuration makes the waves propagate away from (or around) the point of minimization towards the point of maximization. We improve the genetic algorithm by adapting the mutation probability of individual genes according to specific criteria, which depend on the surrounding genes (distributed in two dimensions). This provides the advantages: concentrating the mutations in the areas of genetic inconsistencies and counterbalancing the error of the fitness measurement. The method is applicable for the intelligent design of wave energy harvesters, ultrasonic transducers, and analogue wave computing devices.