时序电路SACO和PSO混合算法的测试矢量生成

提出了一种新的测试矢量生成算法，其使用SCOAP测度对蚁群算法进行参数调整，并在粒子群算法的框架下进行测试矢量生成，再使用调整后的蚁群算法进行测试矢量优化。该算法不仅克服了粒子群算法的容易陷入局部最优等缺点，而且利用电路本身的特性来确定蚁群算法的参数。以国际标准电路为例，实验验证本文的算法，结果表明本算法应用于时序电路的测试矢量生成时，相对于粒子群算法提高了其收敛性，提高了故障覆盖率；相对于蚁群算法压缩了测试矢量集，减少了测试诊断时间。

Test generation of sequential circuits based on the use of the Sandia controllability/observability analysis program to measure ant colony observation (SACO) and partide swarm optimization (PSO)

This paper presents a new test pattern generation algorithm.It uses the Sandia controllability/observability analysis program(SCOAP) to measure ant colony optimization(ACO) with weighted parameters,a procedure which we abbreviate as SACO.Both the weighted ACO and particle swarm optimization(PSO) were implemented for test pattern generation.The adjusted ACO was used to test vector optimization in the PSO algorithm.This avoids the problems associated with local optimumization of PSO.The ACO parameters were determined by the circuit.Finally,the proposed method was verified using a sequential circuit with international standards as an example.The results show that the method described in this article has some advantages for test pattern generation with sequential circuits,since it improves the convergence and fault coverage rate relative to PSO.It also compresses the test vectors and reduces the test time relative to conventional ACO.