An Optimal Test Selection Method Based on Simulated Annealing-Improved Binary Particle Swarm Optimization Algorithm

To solve the non-deterministic polynomial hard(NP-hard) problem of test selection in the design for testability of weapon system, an optimal test selection method based on simulated annealing-improved binary particle swarm optimization(SA-IBPSO) algorithm is proposed to acquire the best complete test set. This algorithm is on the basis of binary particle swarm optimization(BPSO), and uses asynchronous dynamic learning divisors to obtain time-varying contraction factor, which facilitates the global searching speed, guarantees the convergence of BPSO, and abrogates the boundary constraint of particle velocity in BPSO. And the simulated annealing algorithm with probabilistic jumping ability is combined to prevent BPSO from converging to local optimum. Simulation test shows that compared with other algorithms, the proposed algorithm is more effective in acquiring global optimal solution to optimal test selection.