基于NSGA-3与改进贝叶斯网络模型的测试优化选择方法

Test optimization selection method based on NSGA-3 and improved Bayesian network model

现有测试选择问题的解决方案大多基于单目标优化算法与多信号模型,存在指标计算粗糙、解集局限性大等问题。针对这些问题,提出了基于NSGA-3算法与贝叶斯网络模型的测试优化选择方法。描述了改进贝叶斯网络模型,阐述了模型建立方法,介绍了模型的学习能力与对不确定信息的处理能力;根据设计需求所确立的约束条件与目标函数,在改进贝叶斯网络模型基础上,利用基于参考点的非支配排序遗传算法(NSGA-3)来计算测试优化选择方案。以导弹机载雷达某组件为对象,选择故障检测率、隔离率为约束条件,虚警率、误诊率、测试成本、测试数量为优化目标,应用所提方法进行测试优化选择。经验证,所提方法可以有效解决有约束、多目标的测试选择问题,对测试性设计具有指导意义。

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.