A statistical multivariable optimization method using improved orthogonal algorithm based on large data

ABSTRACT

Multivariable optimization under large data environment concerns with how to reliably obtain a set of optimization results from a mass of data that influence the object function complexly. This is an important issue in statistical calculation because the complexity between variable parameters leads to repeated statistical calculation analysis and a significant amount of data waste. A statistical multivariable optimization method using improved orthogonal algorithm based on large data is proposed. Considering the optimization problem with multi-parameters under large data environment, a multi-parameter optimization model used for improved orthogonal algorithm is established based on large data. Furthermore, an extensive simulation study on temperature field distribution of anti-/de-icing component was conducted to verify the validity of the statistical calculation analysis optimization method. The optimized temperature field distribution meets the anti-/de-icing requirements through numerical simulation. Simulation results show that the optimization effect is more evident and accurate than the non-optimized temperature distribution with the optimized results of the proposed method. Results verify the effectiveness of the proposed method.

KEYWORDS:

• Statistical calculation
• multivariable optimization
• large data
• improved orthogonal algorithm
• simulation

Acknowledgements

The authors thank the referees of this paper for their valuable and very helpful comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Long Chen http://orcid.org/0000-0001-6553-1572

Additional information

Funding

This work was supported by National Science and Technology Major Project [grant number 2014ZX04001011]; Beijing Municipal Natural Science Foundation [grant number 3172021]; Defense Industrial Technology Development Program [grant number A0520110009]; State Key Laboratory of Virtual Reality Technology Independent Subject [grant number BUAA-VR-16ZZ-07].