Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)

Parameter Evaluation of 3-parameter Weibull Distribution based on Adaptive Genetic Algorithm

Authors
Chang-Jun Wen, Xin Liu, Xin Cheng
Corresponding Author
Chang-Jun Wen
Available Online June 2016.
DOI
https://doi.org/10.2991/mecs-17.2017.78How to use a DOI?
Keywords
reliability life model; 3-parameter Weibull distribution; maximum likelihood estimation; adaptive genetic algorithm.
Abstract
In the determination of a reliability life model in 3-parameter Weibull distribution, there was large error and inefficient problem in parameter evaluation. Firstly, the maximum likelihood equation was made. Then the deficiency, lied in steps of using traditional genetic algorithm to solve the maximum likelihood equations, was optimized. An adaptive genetic algorithm was obtained that can be used to solve the maximum likelihood equations. Finally, compared the simulation results in MATLAB between adaptive genetic algorithm and traditional genetic algorithm, it can be concluded that the adaptive genetic algorithm was more efficiency and adaptable than the traditional genetic algorithm. This method also provided a reference to solving the similar problem in parameter evaluation.
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Volume Title
Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
Series
Advances in Engineering Research
Publication Date
June 2016
ISBN
978-94-6252-352-4
ISSN
2352-5401
DOI
https://doi.org/10.2991/mecs-17.2017.78How 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  - Chang-Jun Wen
AU  - Xin Liu
AU  - Xin Cheng
PY  - 2016/06
DA  - 2016/06
TI  - Parameter Evaluation of 3-parameter Weibull Distribution based on Adaptive Genetic Algorithm
BT  - Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecs-17.2017.78
DO  - https://doi.org/10.2991/mecs-17.2017.78
ID  - Wen2016/06
ER  -