For the sake of increasing the precision and decreasing the movement uncertainty of articulated arm coordinate measuring machine (AACMM), a calibration method is proposed in this paper, where the accuracy of single point repeatability and volumetric accuracy (indicated by distance precision) are simultaneously considered. Considering the good ability of global exploiting and local exploring granted by chicken swarm optimization (CSO) and tabu search (TS) respectively, the CSO and TS are hybridized to construct the tabu searching-based chicken swarm optimization (TSCSO). To help the TSCSO jump out of the local optimal area when it is trapped in it, a transformational criterion of convergence precision is designed to integrate TSCSO with simulated annealing (SA) to formulate the hybrid algorithm called tabu searching-based chicken swarm optimization with simulated annealing (TSCSO-SA) who simultaneously possesses the good abilities of global exploiting, local exploring, and jumping out of the local optimal area when the population is trapped in it. Moreover, the calibration experiments are conducted based on the accuracy of single point repeatability and volumetric accuracy to verify the effectiveness, efficiency, and suitability of the method and algorithm used in this paper. The results of the experiments and verifications show that the precision and movement uncertainty of AACMM are immensely increased and decreased respectively, furtherly revealing that the method and algorithm proposed in this paper are able to calibrate the structural parameters of AACMM effectively, efficiently, and suitably.