In this manuscript, a novel test data compression (TDC) system is proposes for reducing a test data volume (TDV) for circular scan (CS) architecture. A modified version of meta-heuristic population based optimization approach, hence it is called shuffled shepherd optimization (SSO) and it is used to optimize the conflicting bit minimization (CBM) problem. In CS framework, TDC is reached by upgrading only the conflicting bits among template pattern, real test pattern. Reduced test data volume and test application time are achieved by diminishing the Hamming distance between the currently captured response and the subsequent test vector. The CBM issue is formulated by a traveling salesman problem (TSP). Here, the test vectors are assumed by cities, the HD among test vectors a pair is assumed by distance of interurban. A modified version of the SSO algorithm called as MSSO is applied in combination using mutation operator for solving the issue of CBM. Experimental outcomes display the proposed TDC method using MSSO achieves an average development of 8.36% in compression ratio (CR) as well as 6.77% in TAT is attained by reducing a TDV. This proposed approach outperforms other state-of-art test data compression schemes by providing improved CR, TAT reduction and TDV reduction.

在这份手稿中，提出了一种新颖的测试数据压缩 (TDC) 系统，用于减少循环扫描 (CS) 架构的测试数据量 (TDV)。基于元启发式种群优化方法的改进版本，因此称为混洗牧羊人优化 (SSO)，用于优化冲突位最小化 (CBM) 问题。在 CS 框架中，TDC 是通过仅升级模板模式、真实测试模式之间的冲突位来实现的。通过减小当前捕获的响应与后续测试向量之间的汉明距离，可以减少测试数据量和测试应用时间。CBM 问题由旅行商问题 (TSP) 制定。这里，测试向量由城市假设，一对测试向量中的 HD 由城市间的距离假设。使用变异算子组合应用称为 MSSO 的 SSO 算法的修改版本来解决 CBM 问题。实验结果表明，所提出的使用 MSSO 的 TDC 方法在压缩比 (CR) 中实现了 8.36% 的平均发展，并且通过减少 TDV 实现了 6.77% 的 TAT。通过提供改进的 CR、TAT 减少和 TDV 减少，这种提议的方法优于其他最先进的测试数据压缩方案。