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| 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 |
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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.
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2011, Vol. 32 
