An Improved Teaching-Learning-Based Optimization Algorithm for Sphericity Error Evaluation

DOI

10.2991/amcce-18.2018.64How to use a DOI?

Keywords

Teaching-Learning-Based, Optimization Algorithm, Sphericity Error Evaluation

Abstract

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.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - 10.2991/amcce-18.2018.64
ID  - Yang2018/05
ER  -