测试节点优化是电路板测试诊断设计优化过程中的一个基本问题,属于经典的N-P难题,但目前用于测试点优化的算法存在搜索速度慢、不收敛等问题,因此提出一种基于遗传粒子群优化算法。该方法通过建立电路测试节点的“故障一测试”矩阵,用遗传算法对数学模型进行优化,并采用粒子群算法搜索实现了快速求解。与传统方法相比较,该方法搜索速度快,优化效果明显,已在工程实践中得到应用。

Test node optimization problem is a key problem of diagnostic design for circuit, which belongs to an N-P completeness problem Algorithms being applied for the test node optimization problem have some problems such as slow searching speed and no convergence, so a kind of method based on genetic particle swarm optimization algorithm was presented to solve the problem. It optimizes the test node's “fault-test” matrix by genetic algorithm and searches quickly by particle swarm optimization. Searching with the method is faster than traditional methods, and the optimal results are also better. The method has been used in engineering practice.