In order to solve the problem that the conventional heuristic optimization algorithms is inefficient in evaluating roundness error and easy to fall into local optimal solution,an improved crow search algorithm was introduced to evaluate roundness error.The mathematical model for evaluating roundness errors of crow search algorithm was formulated based on the minimum zone circle(MZC) fitting criterion,and the weight coefficient was introduced to enhance the global search capability of the algorithm.At the same time,the initialization of the starting position in proximity to the center of the least squares circle was implemented to improve the algorithm′s search efficiency.Finally,the accuracy and precision of the proposed algorithm were validated through simulations and experiments.It is found that the global search ability of the improved crow search algorithm is significantly improved compared with genetic algorithm (GA),particle swarm optimization (PSO) and traditional crow search algorithm (CSA) by comparing multiple sets of data.