An Improved Particle Swarm Optimization Algorithm Applied to the Unified Evaluation of Circularity Error

摘要：

　　Minimum zone circle (MZC),minimum circumscribed circle (MCC),maximum inscribed circle (MIC) and least square circle (LSC) are four common methods used to evaluate circularity errors.A novel particle swarm optimization algorithm based on self-adapted comprehensive learning (ACL-PSO) is proposed to evaluate circularity errors with real coded strategy.In the algorithm,population learning mechanism and velocity mutation strategy are adopted.In the meantime,ACL-PSO is applied to the unified evaluation of circularity error.The experiment results evaluated by different methods indicate that the proposed algorithm not only converges to the global optimum rapidly,but also has good stability,and it is easy to generalize.

关键词： circularity error evolutionary strategy steepest descent PSO population centroid

作者: Zhongyong Wu Jin Gou Changcai Cui

作者单位: College of Computer Science and Technology, Huaqiao University, XiaMen 361021, China College of Mechanical Engineering and Automation, Huaqiao University, XiaMen 361021, China

会议类型: 国际会议

会议名称: the 2010 International Conference on Frontiers of Manufacturing and Design Science(第一届制造与设计科学国际会议(ICFMD 2010))

会议地点: 重庆

会议语种:英文

页码: 3937-3941

在线出版日期: 2010-12-11（万方平台首次上网日期，不代表论文的发表时间）