A new concept genetic algorithm (GA) has been implemented and tested for the use in the 2-D and 3-D Particle Tracking Velocimetry (PTV). The algorithm is applicable to particle images with larger (greater than 2000) number of particles without losing the excellent accuracy in the particle matching results. This is mainly due to a new fitness function as well as unique genetic operations devised especially for the purpose of particle matching problem. The new fitness function is based on the relaxation of movement of a group of particles and is particularly suited for an increased density of particle images. The unique genetic operations give rise to the concentration of more fit genes in the forward part of the gene strings where the crossover and mutation processes are suppressed. The new algorithm also profits from the new genetic encoding scheme which can deal with the loss-of-pair particles (i.e., those particles which exist in one frame but do not have their matching pair in the other frame), a typical problem in the real image particle tracking velocimetry. In the present study, the new method is tested with 2-D and 3-D synthetic as well as real particle images with a large number of particles.

中文翻译：

---

使用遗传算法的粒子跟踪测速

一种新概念的遗传算法 (GA) 已实施并测试用于 2-D 和 3-D 粒子跟踪测速 (PTV)。该算法适用于粒子数较大（大于2000）的粒子图像，而不会损失粒子匹配结果的优良精度。这主要是由于新的适应度函数以及专门为粒子匹配问题设计的独特遗传操作。新的适应度函数基于一组粒子的运动松弛，特别适用于增加粒子图像的密度。独特的遗传操作使更适合的基因集中在基因串的前部，其中交叉和突变过程被抑制。新算法还受益于新的遗传编码方案，该方案可以处理丢失对粒子（即存在于一帧中但在另一帧中没有匹配对的粒子），这是一个典型的问题。实像粒子跟踪测速。在本研究中，新方法用 2-D 和 3-D 合成以及具有大量粒子的真实粒子图像进行了测试。