基于ACBPSO的板级电路测试性优化设计  

Optimizing design of board level circuit testability based on adaptive chaotic binary particle swarm optimization

作　　者：王杨[1] 刘波峰[1] 谭阳红[1] 袁卿卿 李涛[1] 王文强[1] 

机构地区：[1]湖南大学电气与信息工程学院,长沙410082

出　　处：《电子测量与仪器学报》2015年第6期823-829,共7页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(61102039;51107034;50277010);湖南省自然科学基金(07JJ6132)资助项目

摘　　要：边界扫描布局优化是复杂系统测试性设计的重要内容,属于典型的组合优化问题。提出了一种基于自适应混沌二进制粒子群优化算法(ACBPSO)的板级电路测试性设计最小化优化方法。该算法利用混沌运动的遍历性来初始化粒子群参数,惯性权重则根据粒子群的早熟收敛程度自适应调整。随着算法迭代运行,离散粒子群算法(BPSO)的随机性越来越强,缺乏后期的局部搜索能力,故引入新的概率映射函数。仿真实例验证了该算法有效地克服了二进制粒子群的早熟收敛现象,提高了搜索效率,具有良好的优化效果。Boundary scan layout optimization is important content of testability design for complicated systems, which belongs to the typical combinatorial optimization problem. An adaptive chaotic binary particle swarm optimi- zation （ACBPSO） board-level circuit design for testability minimization optimization methods was proposed. The er- godic of chaos has been used to initialize the parameters of the particles, and the inertia weight is adjusted adaptive- ly according to the swarm＇s premature convergence degree, and the inertia weight is adjusted adaptively according to the swarm＇s premature convergence degree. With the iteration going on, the randomness is more and more power- ful, so binary PSO is lack of local exploration which instructs the improvement of BPSO. The new probability map- ping function is used to solve the drawback. The simulation results demonstrate that the proposed algorithm over- come the drawback of prematurity convergence of BPSO, improve the global search ability and has good results.

关 键 词：测试性设计优化 改进二进制粒子群 混沌优化 自适应权重 早熟 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置] TN06[自动化与计算机技术—控制科学与工程]