测试点优化选择是复杂装备测试性设计的重要环节, 本文提出一种用于解决测试点优化选择问题的离散萤火虫算法(DFA)。首先建立了测试点优化选择问题的数学模型, 接着对传统的萤火虫算法(FA)进行了离散化改进, 给出了离散化萤火虫算法的实施步骤, 并分析了不同的吸引度函数和二值化函数(sigmoid和tanh函数)对算法结果的影响。最后针对5个不同规模的实际系统验证了离散萤火虫算法的有效性, 并与粒子群算法(PSO)和遗传算法(GA)等传统的元启发式搜索算法的计算性能进行了比较分析。结果显示: 在满足系统要求的故障检测率和故障隔离率的前提下, 利用本文提出的离散萤火虫算法得到的5个系统测试代价最优值分别比PSO算法和GA算法平均降低了10.1%和14.6%。实验结果表明: 离散萤火虫算法能快速收敛到更高质量的全局最优解, 避免过早收敛而陷入局部最优值, 对于解决大型复杂装备的测试点优化选择问题具有很好的应用前景。

Optimization selection of test points is an important step of testability design for complex equipment, so a Discrete Firefly Algorithm(DFA) used for solving optimization selection problem of test points was proposed. First of all, the mathematical model of optimization selection problem of test points was built, then discretization improvement was conducted on the traditional firefly algorithm, and the implementation steps of the DFA were given, later the effect of different attraction functions and binarization functions (sigmoid and tanh functions) on the result of the algorithms was also analyzed. Finally, The DFA was applied to five real systems with different sizes to verify the effectiveness, and the computational efficiency of DFA was compared with particle swarm optimization (PSO) and genetic algorithm (GA). In premise of complying with fault detection rate and fault isolation rate the system requires, optimal value of test cost for 5 systems from proposed DFA respectively reduced by 10.1% and 14.6% compared with PSO algorithm and GA algorithm. The experimental result shows: DFA can quickly converge to the global optimal solution of higher quality, and it can avoid trapping into local optimal solution, so it has very good application prospect to solve optimization solution problem of test points for large-scale complex equipment.