|
|
Sphericity Error Evaluation with Double-quick Artificial Bee Colony Algorithm |
LUO Jun,WU Hua,WANG Qiang |
Key Lab. for Optoelectronic Technology & Systems of the Ministry of Education, Chongqing University, Chongqing 400030, China |
|
|
Abstract Artificial Bee Colony(ABC) Algorithm is used to evaluate the sphericity error, and the evaluation model of the minimum zone sphericity error is given as well. Based on the peculiarity of sphericity error evaluation, the ABC algorithm is improved by following ways: Firstly, a set of bees is introduced from employers according to probability to find feasible solutions in the neighborhood of the best solution currently, and the convergence rate is rapidly improved. Besides, in order to enhance the ability of the algorithm to break away from the local optimum, randomly choose some scouts according to probability to find feasible solutions in the neighborhood of the best solution currently. The feasibility of the new algorithm was validated according to a typical testing function. The experimental results of two sets using the improved ABC algorithm and several different typical Swarm Intelligence(SI) algorithms proved that the improved ABC algorithm has advantages of fast convergence speed,high accuracy and strong robustness when evaluate the sphericity error. This improved algorithm applies to the measurement and testing of precision instruments.
|
|
|
|
|
|
[1]温秀兰,宋爱国. 基于免疫进化算法的球度误差评定[J].计量学报, 2005, 26(1):12-15.
[2]Cui C C, Che R S, Ye D, et al.Sphericity error evaluation using the genetic algorithm[J].Optics and Precision Engineering, 2002, 10(4):333-339.
[3]Wen X L, Li H S, et al. Sphercity Error United Evaluation Using Particle Swarm Optimization Technique[C]// ICEMI. The Ninth International Conference on Electronic Measurement & Instruments. Beijing, China, 2009, 1:156-161.
[4]Wen X L, Song A G. An Improved Genetic Algorithm for Sphericity Error Evaluation[C]// ICNNSP. Int Conf Neural Networks & Signal Processing. Nanjing, China, 2003, 11:14-17.
[5]Huang M F, Yu X, Zhong Y R, et al. Sphericity Error Evaluation Based on an Improved Particle Swarm Optimization[C]// ICGEC. 2009 Third International Conference on Genetic and Evolutionary Computing.Guilin, China, 2009, 657-660.
[6]Zhang K. Minimum Zone Evaluation of Sphericity Error Based on Ant Colony Algorithm[C]// ICEMI. The Eighth International Conference on Electronic Measurement & Instruments. Xian, China, 2007, 2:535-538.
[7]Karaboga D, Akay B A. Comparative study of artificial bee colony algorithm[J]. Applied Mathematics and Computation, 2009, 214(1):108-132.
[8]Karaboga D, Basturk B. On the performance of artificial bee colony(ABC) algorithm[J]. Applied Soft Computing, 2008, 8(1): 687-697.
[9]罗钧, 李研. 具有混沌搜索策略的蜂群优化算法[J]. 控制与决策, 2010, 25(12):1913-1916.
[10]Fan K C, Lee J C. Analysis of minimum zone sphericity error using minimum potential energy theory[J]. Precision Engineering, 1999, 23(2):65-72.
[11]Wang M, Cheraghi S H, Masud A S M. Sphericity Error Evaluation: Theoretical Derivation and Algorithm Development[J].IIE Transactions, 2001, 33(4):281-292.
[12]茅健. 基于数学定义的公差建模与误差评定技术的研究[D].杭州:浙江大学,2007. |
|
|
|