An Improved Ant Colony Optimization Based Particle Matching Algorithm for Time-Differential Pairing in Particle Tracking Velocimetry

A new improved ant colony optimization (ACO) based algorithm has been developed for temporal particle matching in 2-D and 3-D particle tracking velocimetry (PTV). Two of the present authors have already applied the ant colony optimization (ACO) based algorithm effectively and successfully to the time differential particle pairing process of particle tracking velocimetry (PTV). In the present study, the algorithm has been further improved for the reduced computation time as well as for the same or slightly better particle pairing results than that of the authors' previous ACO algorithm. This improvement is mainly achieved due to the revision of the selection probability and pheromone update formulae devised specially for the purpose of accurate and fast computation. In addition, the new algorithm also provides better matching results when dealing 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. The performance of the new improved algorithm is tested with 2-D and 3-D standard particle images with successful results.