Abstract：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 discrete particle swarm optimization (SA-DPSO) algorithm is proposed to acquire the best complete test set. This algorithm is on the basis of discrete particle swarm optimization (DPSO), and uses asynchronous dynamic learning divisors to obtain time-varying contraction factor, which facilitates the global searching speed, guarantees the convergence of DPSO, and abrogates the boundary constraint of particle velocity in DPSO. And the simulated annealing algorithm with probabilistic jumping ability is combined to prevent DPSO 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.
王大为, 邵志江, 张健, 刘泰涞, 朱显明. 一种基于改进SA-DPSO的装备测试性优化设计方法[J]. 空天防御, 2023, 6(1): 49-55.
WANG Dawei, SHAO Zhijiang, ZHANG Jian, LIU Tailai, ZHU Xianming. An Optimal Design of Equipment Testability Based on SA-DPSO Algorithm. Air & Space Defense, 2023, 6(1): 49-55.
