Abstract：A differential bee colony algorithm was proposed to evaluate spatial straightness error. Firstly, based on the geometrical product specification and verification(GPS)tolerance standard, the mathematical model of spatial straightness error evaluation conforming to the minimum region is obtained by least square fitting calculation. Secondly, the scaling factor of the differential evolution algorithm was improved, and the improved differential evolution algorithm and artificial bee colony algorithm were mixed and iteratively optimized, through the test function simulation, the differential bee colony algorithm had certain advantages in calculation accuracy and convergence speed. Finally, two evaluation cases are studied and compared. the results show that compared with particle swarm optimization algorithm, hybrid teaching and learning algorithm, least square algorithm and fish swarm hybrid optimization algorithm, the calculation accuracy of spatial straightness evaluation is improved by 83.9%, 54.5% and 54.6%, respectively. compared with differential evolution algorithm and artificial bee colony algorithm, the convergence speed is improved obviously.
Li J S, Lei X Q, Xue Y J, et al. Geometric Approximation Searching Algorithm for Spatial Straightness Error Evaluation［J］. Modern Instrumentation, 2013, 2(1): 1-6.
Chen Y, Han B, Xu G Q, et al. Spatial straightness error evaluation of improved whale optimization algorithm［J］. Mechanical Science and Technology for Aerospace Engineering, 2022, 41(7): 1102-1111.
Zhao F X, Zhang L N, Zheng Y H, et al. Spatial straightness Error Evaluation and its Uncertainty Estimation based on GPS［J］. Mechanical Strength, 2008, 137(3): 441-444.
Yu D G, Ning L, Meng X H. Error Evaluation Method for the Axis Straightness of Deep hole Based on Least square Principle［J］Modular Machine Tool and Automatic Manufacturing Technique, 2014, 479(1): 39-41, 45.
［15］
Dervis K, Bahriye B. Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems［J］. Foundations of Fuzzy Logic and Soft Computing, 2007, 4529(1): 789-798.
Wang C, Ren C, Li B R, et al. Research on Straightness Error Evaluation Method Based on Search Algorithm of Beetle［C］／／Advanced Manufacturing and Automation VIII, 2019, 484(2): 368-374.
Wen Y P. Research on Dynamic Step-size Bacteria Foraging Optimization Algorithm for Evaluation of Hydraulic Cylinder Axis Straightness Error［J］. Coal Mine Machinery, 2017, 38(10): 32-34.
Ye M, Tang D B. Study on the Evaluation of Straightness Error Via Hybrid Least Squares and Artificial Fish Swarm Algorithm［J］. Mechanical Science and Technology, 2014, 33(7): 1013-1017.
Chen J B, Wang C, Wang S H. Research on Evaluation Method of Spatial Straightness for Variable Step Beetle Antennae Search Algorithm［J］. Tool Engineering, 2018, 52(8): 136-138.
Yang Y, Li M, Gu J J, et al. Application of Hybrid Teaching-learning-based Optimization Algorithm in Spatial Straightness Evaluation［J］. Acta Metrologica Sinica, 2018, 39(1): 15-19.
［9］
刘宏志. 人工蜂群与差分进化算法研究及其应用［D］. 沈阳:东北大学, 2015.
［14］
Rainer S, Kenneth P. Differential Evolution—A Simple and Efficient Heuristic for global Optimization over Continuous Spaces［J］. Journal of Global Optimization, 1997, 11(4): 341-359.
Lin M J, Luo F, Su C H, et al. A improved hybrid intelligent extreme learning machine［J］. Control and Decision, 2015, 30(6): 1078-1084.
2023, Vol. 44 
