An Improved Teaching-Learning-Based Optimization Algorithm for Sphericity Error Evaluation
Yang Yang, Ming Li, JingJun Gu
Available Online May 2018.
- https://doi.org/10.2991/amcce-18.2018.64How to use a DOI?
- Teaching-Learning-Based, Optimization Algorithm, Sphericity Error Evaluation
- In order to improve the accuracy and the convergence speed of the sphericity error, an improved teaching and learning algorithm is proposed to evaluate the sphericity error. Based on the basic teaching-learning-based optimization, the initial solution quality is improved by logistic chaotic initialization; At the end of each iteration, the interpolation algorithm is applied to the global optimal solution to further improve the search accuracy of the algorithm. Finally, one group of sphericity error algorithm though the measurement data in the related literature is verified the effectiveness of the ITLBO, the test result show that the ITLBO algorithm has advantages in the calculating accuracy and iteration convergence speed, and it is very suitable for the application in the sphericity error evaluation.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Yang Yang AU - Ming Li AU - JingJun Gu PY - 2018/05 DA - 2018/05 TI - An Improved Teaching-Learning-Based Optimization Algorithm for Sphericity Error Evaluation BT - Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018) PB - Atlantis Press SP - 374 EP - 379 SN - 2352-5401 UR - https://doi.org/10.2991/amcce-18.2018.64 DO - https://doi.org/10.2991/amcce-18.2018.64 ID - Yang2018/05 ER -