基于模拟退火粒子群算法的不可靠测试点优化

针对实际复杂系统测试与诊断时存在虚警和漏检的情况问题,提出在不可靠测试条件下,基于模拟退火粒子群(SA-PSO)算法的测试点优化方法。首先综合考虑不可靠测试条件下测试点的故障检测能力、故障隔离能力及结果信任度设计了评价测试点性能的启发函数;然后,将该启发函数与测试费用最小原则相结合,并根据测试性指标的要求,构建确保测试点最优的适应度函数;最后,设计基于模拟退火粒子群算法的不可靠测试点优化步骤,并用阿波罗发射系统实例验证了该算法的优越性。结果表明SA-PSO算法能够在满足测试性指标的要求下获得最小测试费用的测试点集,其故障检测率、隔离率都优于贪婪算法及遗传算法。

Test point optimization under unreliable test based on simulated annealing particle swarm optimization

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.