摘要
为了解决测试性设计中测试优化选择这一非确定性多项式难题(non-deterministic polynomial hard,NPhard),提出一种改进模拟退火-离散粒子群算法(simulated annealing-discrete particle swarm optimization,SADPSO)用于求解最优完备测试集。该算法首先以离散粒子群算法(DPSO)为基础框架,采用异步变化的学习因子,产生时变的压缩因子,以增强DPSO算法的全局搜索能力,确保其收敛性,并取消了对速度的边界限制;然后,与具有概率突跳能力的模拟退火算法(SA)相结合,以避免DPSO算法在求解过程中陷入局部最优;最终,基于对某发控系统测试点进行优选,经验证,所提算法能够显著提升测试优化效率。
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.
作者
王大为
邵志江
张健
刘泰涞
朱显明
WANG Dawei;SHAO Zhijiang;ZHANG Jian;LIU Tailai;ZHU Xianming(Shanghai Electro-Mechanical Engineering Institute,Shanghai 201109,China)
出处
《空天防御》
2023年第1期49-55,共7页
Air & Space Defense
基金
空军装备预先研究项目(403020101)。
关键词
相关性矩阵
测试优化
模拟退火
离散PSO算法
自适应方法
correlation matrix
test optimization
simulated annealing
discrete particle swarm optimization
adaptive method
