Optimization method for diagnostic sequence based on improved particle swarm optimization algorithm  

作　　者：Lian Guangyao Huang Kaoli Chen Jianhui Gao Fengqi 

机构地区：[1]Ordnance Engineering Coll., Shijiazhuang 050003, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2009年第4期899-905,共7页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(60771063).

摘　　要：To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （QPSO） algorithm. By a precedence ordering coding, the diagnostic sequence optimization can be translated into a precedence ordering problem in the multidimensional space of swarm. It can get the optimizing order quickly by using the powerful and quick search capability of QPSO algorithm, and the order is the diagnostic sequence for the system. The realization of the method is simpler than other methods, and the results are more excellent than others, and it has been applied in the engineering practice.To realize the requirement of diagnostic sequence optimization in the process of design for testability, the authors put forward an optimization method based on quantum-behaved particle swarm optimization （QPSO） algorithm. By a precedence ordering coding, the diagnostic sequence optimization can be translated into a precedence ordering problem in the multidimensional space of swarm. It can get the optimizing order quickly by using the powerful and quick search capability of QPSO algorithm, and the order is the diagnostic sequence for the system. The realization of the method is simpler than other methods, and the results are more excellent than others, and it has been applied in the engineering practice.

关 键 词：diagnostic sequence optimization design for testability intelligent optimization QPSO algorithm 

分 类 号：N94[自然科学总论—系统科学]