An improved particle swarm optimization-simulated annealing algorithm (IPSO-SAA) is proposed to enhance the measurement accuracy of the coordinate measuring machine (CMM) by identifying the optimal measurement area for the measured object. First, the distribution pattern of volumetric errors within the CMM measurement space is analyzed. Individual geometric error models are fitted using the least squares method, and an optimization model for the point errors in the CMM space is established. The proposed IPSO-SAA method, which combines adaptive weighting, adaptive disturbance force and simulated annealing algorithm, outperforms conventional particle swarm optimization (PSO) and adaptive particle swarm optimization (APSO) algorithms. Comparative experiments show that IPSO-SAA is superior to PSO and APSO algorithms in terms of the best, worst, mean, and standard deviation values. Additionally, the optimization speed is increased by 45. 1% and 29. 2% , respectively. The results obtained from the IPSO-SAA algorithm identification indicate that, for a planning optimization space of 30 mm×30 mm×30 mm, the optimal measurement area in the CMM identified by the IPSO-SAA algorithm is 206 mm ≤ X ≤ 236 mm, 350 mm ≤ Y ≤ 380 mm, and - 262 mm ≤ Z ≤ - 232 mm. Comparative experiments using a high-precision standard ball, with a diameter of 15. 874 7 mm and a sphericity of 50 nm, demonstrate that when placed within the optimal measurement area in the CMM, the minimum diameter measurement error of the standard ball is 1. 7 μm, validating the correctness of the proposed method. The method presented in this study is general and can be used to determine the optimal measurement area of CMM for other measured objects.