A method of test points optimization selection based on improved bacterial foraging algorithm

Wenkui Hou, Zhiming Zhang
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引用次数: 2

Abstract

In this paper, an optimal selection method of the test points in the field of test-ability design is provided. Based on bacterial foraging algorithm and improved particle swarm optimization algorithm. This proposed method, which is named SC_BFO (Swarm Cooperation Bacteria Foraging Optimization), is applied in optimization selection of test points. Firstly, in order to ensure the availability of the algorithm, the reliability, accuracy and robustness of the algorithm are tested by using the classical test function. Subsequently, the optimization algorithm is used in a case of test-ability design and the simulation results show that FDR (Fault Detection Rate) and FIR (Fault Isolation Rate) are both improved as well as the cost of test is reduced by using the SC_BFO algorithm.
基于改进细菌觅食算法的测试点优化选择方法
本文提出了可测试性设计领域中测试点的优化选择方法。基于细菌觅食算法和改进的粒子群优化算法。将该方法命名为SC_BFO (Swarm Cooperation Bacteria Foraging Optimization),用于测试点的优化选择。首先,为了保证算法的可用性,利用经典测试函数对算法的可靠性、准确性和鲁棒性进行了测试。随后,将该优化算法应用于可测试性设计案例,仿真结果表明,SC_BFO算法不仅提高了故障检测率(FDR)和故障隔离率(FIR),而且降低了测试成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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