Test point placement optimization based on multi-signal flow graph and differential evolution algorithm

To improve the test point placement optimization efficiency in system testability design process, a test point placement optimization method based on multi-signal flow graph and differential evolution algorithm is proposed. In this method, an object system model based on multi-signal flow graph is firstly built. Based on this model, a dependency matrix of tests and failure modes is generated. Then, according to the flexible demandsof test point placement forfault detection rate, fault isolation rate and number of test points, the dependency matrix and differential evolution algorithm arecombined to find the optimal test point placement solution. Simulation and practical application cases demonstrate the effectiveness of this method. Moreover, the results of the comparison experiment with the conventional method based on genetic algorithm also demonstrate that the proposed method can obtain the optimum test point combination more stably and faster, which makesit more suitable for the testability design of large scale complex systems.