用于测试序列优化的DPSO-WAO*算法研究

Research on DPSO-Weight_AO* Algorithm for Optimal Test-sequencing Problem

摘要:

针对现有测试序列优化算法所存在的计算效率及优化性能间的矛盾,结合离散粒子群算法(DPSO),提出了基于加权Huffman编码的启发式评估函数,对传统AO*算法进行改进,提出了DPSO-WAO*(DPSO-Weight_AO*)算法。实例证明,基于加权Huffman编码的启发式评估函数更为准确地评估了全局测试成本,在取消了成本回溯的情况下,算法仍能保持较高的优化性能,且有效地降低了计算复杂度,对于大型系统的测试序列设计、可测试性分析及故障诊断等具有重要意义。

Abstract:

Aim at the contradiction between the calculation efficiency and optimization performance of the existing test sequence optimization algorithm, combining with the discrete particle swarm optimization algorithm, a heuristic evaluation function based on weighted Huffman coding was proposed to improve the traditional AO* algorithm, called DPSO-WAO*(DPSO-Weight_AO*)algorithm. The heuristic evaluation function based on weighted Huffman coding was proved by Examples that it can more accurately evaluate the global test cost. In the case of canceling the cost back, the DPSO-WAO* algorithm can still maintain high optimization performance and effectively reduce the computational complexity of the algorithm. It is of great significance for test sequence design, testability analysis and fault diagnosis of large system.