Volume 39, Number 2, April 2021
|Page(s)||414 - 422|
|Published online||09 June 2021|
Test optimization selection method based on NSGA-3 and improved Bayesian network model
Coast Guard Academy, Naval Aviation University, Yantai 264000, China
Most of the solutions to existing test selection problems are based on single-objective optimization algorithms and multi-signal models, which maybe lead to some problems such as rough index calculation and large solution set limitations. To solve these problems, a test optimization selection method based on NSGA-3 algorithm and Bayesian network model is proposed. Firstly, the paper describes the improved Bayesian network model, expounds the method of model establishment, and introduces the model's learning ability and processing ability on uncertain information. According to the constraints and objective functions established by the design requirements, NSGA-3 is used to calculate the test optimization selection scheme based on the improved Bayesian network model. Taking a certain component of the missile airborne radar as an example, the fault detection rate and isolation rate are selected as constraints, and the false alarm rate, misdiagnosis rate, test cost, and test quantity are the optimization goals. The method of this paper is used for test optimization selection. It has been verified that this method can effectively solve the problem of multi-objective test selection, and has guiding significance for testability design.
Key words: test optimization selection / Bayesian network model / multi-objective / NSGA-3 / fault detectio
关键字 : 测试优化选择 / 贝叶斯网络模型 / 多目标 / NSGA-3 / 故障检测
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
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