科研之友
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摘要
An improved artificial bee colony (IABC) optimization algorithm for the accurate evaluation of minimum zone axis straightness error from a set of coordinate measurement data points was proposed. In the proposed algorithm, the opposition-based learning method was employed to produce initial population and scouts, the employed bees used greedy selection mechanism to update the best food source achieved so far one by one, and a new search mechanism inspired by differential evaluation was used for onlookers. The nonlinear mathematical model for axis straightness error evaluation and the fitness function of IABC were introduced in detail. Four classical test functions were selected in the experiments; the simulation results verified the feasibility of IABC algorithm. According to two practical examples, the results obtained by the IABC algorithm are more accurate and efficient than other conventional methods. It is a unified approach for other form and position error evaluations and is well suited for high-precision measuring equipments such as the CMM.
- 出版日期2014-3
- 单位重庆大学
