离散萤火虫算法的复杂装备测试点优化选择

发布时间：2018-04-09 01:43

  本文选题：可测试性设计　切入点：测试点优化选择　出处：《光学精密工程》2017年05期

【摘要】：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。
[Abstract]:The optimal selection of test points is an important link in the testability design of complex equipment. In this paper, a discrete firefly algorithm is proposed to solve the problem of optimal selection of test points.Firstly, the mathematical model of optimal selection of test points is established, and then the discretization of the traditional firefly algorithm (FAA) is improved, and the implementation steps of the discrete firefly algorithm are given.The effects of different attraction functions and binarization functions (sigmoid and tanh functions) on the results of the algorithm are analyzed.Finally, the effectiveness of the discrete firefly algorithm is verified for five different scale systems, and the computational performance of the traditional meta-heuristic search algorithm, such as particle swarm optimization (PSO) and genetic algorithm (GA), is compared and analyzed.The results show that under the premise of fault detection rate and fault isolation rate which meet the requirements of the system, the optimal test cost of the five systems obtained by using the discrete firefly algorithm proposed in this paper is 10.1% and 14.6% lower than that of the PSO algorithm and the GA algorithm, respectively.The experimental results show that the discrete firefly algorithm can quickly converge to a higher quality global optimal solution, avoid premature convergence and fall into a local optimal value, and has a good application prospect for solving the problem of test point selection for large and complex equipment.
【作者单位】： 中国科学院长春光学精密机械与物理研究所;
【基金】：国家重点实验室研究基金资助项目(No.SKLLIM0902-01)
【分类号】：TP18
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本文编号：1724321