Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)

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

Authors
Yang Yang, Ming Li, JingJun Gu
Corresponding Author
Yang Yang
Available Online May 2018.
DOI
https://doi.org/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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
Part of series
Advances in Engineering Research
Publication Date
May 2018
ISBN
978-94-6252-508-5
ISSN
2352-5401
DOI
https://doi.org/10.2991/amcce-18.2018.64How to use a DOI?
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  - 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
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  -