Test Selection based on Improved Binary Particle Swarm Optimization

Test Selection in DFT (design for test) is known to be a NP-complete problem. Applying particle swarm optimization algorithm to test selection is firstly proposed in this paper. By analyzing characteristics of test selection, it constructs particles and velocities of BPSO (binary particle swarm optimization); It optimizes the particles using the fitness, which includes the indexes of test selection, and adds multiform inertial weight to BPSO according to its characteristic of getting into local optimal easily. The experimental results show that the proposed algorithm can earlier achieve higher fault detection rate, isolation rate and more compact test sets when compared to other similar test selection algorithms.