用多目标遗传算法获取压电材料参数

Using multi-objective genetic algorithm to determine parameters of piezoelectric materials

多目标遗传算法是在遗传算法的基础上,利用在多个给定区域内的遗传优化性能,反演出这些特定区域内的优化特性。本文基于瑞利波的有效介电常数理论,将压电材料的弹性常数CE、压电常数e、介电常数εs看作特定区域的待优化参数,利用压电材料(本文用石英、铌酸锂晶体为例)的同一切割面不同传播方向的表面波速度,运用多目标遗传算法,通过概率分区优化,逐次逼近真值,成功地同时反演出了它们的弹性常数、压电常数与介电常数。证明了该方法同样适用于获取新材料的参数。

Multi-objective genetic algorithm, based on traditional genetic algorithm, has the characteristic of optimizing the parameters in several given regions. In this paper, based on the theory of effective surface permittivity, we regard the elastic constants CE, piezoelectric constants e, dielectric constants εS of the piezoelectric materials as the parameters in the given region which are to be optimized. We use multi-objective genetic algorithm to reverse the process of getting velocities of the piezoelectric crystals (as the examples of quartz and Lithium Niobate) with different propagating angles in the same plane and extend the search range of every parameter using subregion approach based on probability to determine all the acoustical physical constants of piezoelectric materials at the same time successfully. It proves that multi-objective genetic algorithm is also fit for getting parameters of the new materials.