Random DBPSO algorithm application in the Optimal Test-sequencing Problem of complicated electronic system

Abstract

An algorithm of improved AO* based on discrete binary particle swarm optimization (DBPSO) with additional random item is proposed, which can solve the Optimal Test-sequencing Problem (OTP) in large-scale complicated electron system. DBPSO optimizes the test sets which can isolate the expanded node in AO* algorithm to decrease the number of node. The result of real operation show that this algorithm not only reduces the computational complexity, cuts down the test cost, shorts the test time; but also avoids the “computational explosion” when the test set is too large. Comparing with inertia weight, the particle's velocity is determined by previous velocity, own experience, public knowledge and random behavior defined by the additional random factor which helps to get the global optimization solution. Simulation results show that the method with the random factor is better than inertia weight and constriction factor.