基于MOPSO-NSGA2算法的测试优化选择方法

A Test Optimization Selection Method Based on MOPSO-NSGA2 Algorithm

测试优化选择是测试性设计至关重要的一个步骤,主要针对测试不可靠条件下的测试优化选择问题进行了研究。首先将该问题还原为多目标问题来分析,在此基础上以测试数量、测试成本、虚警率为目标,故障检测率、隔离率为约束条件建立了问题的数学模型;然后以贝叶斯网络测试性模型为基础,利用提出的MOPSO-NSGA2算法求解该问题;最后利用所提算法对某导弹机载无线电高度表开展测试优化选择设计,并与MOPSO算法、NSGA-2算法进行对比,验证了方法的有效性与实用性。

Test optimization selection is an important step in testability design. This paper mainly studies the test optimization selection problem under the condition of unreliable test. Firstly,the problem is reduced to a multi-objective problem for analysis. On this basis,the mathematical model of the problem is established with the test quantity,test cost and false alarm rate as the objectives,and the fault detection rate and isolation rate as the constraints. Then,based on the Bayesian network testability model,the MOPSO-NSGA2 algorithm is used to solve the problem. Finally,the method is used to make test optimization selection for the radio altimeter of a certain missile,and a comparison is made with MOPSO algorithm and NSGA-2 algorithm.The results verify the effectiveness and feasibility of the method.