Considering the false alarm and miss detection during testing and diagnosis of complex system, a new method was proposed to solve test selection problems under unreliable test based on Simulated Annealing Particle Swarm Optimization (SA-PSO) algorithm. Firstly, a heuristic function was established to evaluate the capability of test point detection, coverage and reliance. Then, combining the heuristic function with the least test cost principle and considering the requirement of testability targets, a fitness function to ensure optimal selection was designed. Lastly, the process and key steps of SA-PSO were introduced and the superiority of this algorithm was verified by simulation results of launch system of Apollo. The results show that the proposed method can find the global optimal test points. It can minimize test cost on requirement of testability targets and has higher fault detection and isolation rate compared with greedy algorithm and genetic algorithm.

For the problem of test selection for complex system, a test selection optimization based on Simulated Annealing Binary Particle Swarm Optimization (SA-BPSO) algorithm was adopted. The probabilistic jumping ability of simulated annealing algorithm was used to overcome the deficiencies of the particle swarm being easily fall into local optimal solution. The process and key steps of the algorithm for test selection in complex system were introduced, and the complexity of the algorithm was analyzed. The simulation results show that the algorithm has better performance in running time and testing cost compared to genetic algorithm, thus the algorithm can be used to optimize test points of complex system.