Multidimensional Fitness Function DPSO Algorithm for Analog Test Point Selection
Multidimensional Fitness Function DPSO Algorithm for Analog Test Point Selection
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DOI：
10.1109/tim.2009.2021643
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发表时间：
2010-05
影响因子：
5.6
通讯作者：
Ronghua Jiang;Houjun Wang;Shulin Tian;B. Long
中科院分区：
文献类型：
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作者：
Ronghua Jiang;Houjun Wang;Shulin Tian;B. Long
提出了一种新的多维适应度函数离散粒子群优化算法来优化模拟测试点的选择。该方法利用故障隔离率和测试点数来构造多维适应度函数来搜索全局最小测试点集，并利用精英集得到多个可能的最优解。通过对其他优化测试点方法的实验验证，证明了该方法的有效性。结果表明，本文提出的算法不仅降低了计算复杂度，而且缩短了时间消耗。它对于大规模模拟电路特别有用。
A novel multidimensional fitness function discrete particle swarm optimization algorithm is proposed to optimize analog test point selection. The proposed method uses fault isolation rate and the number of test points to formulate a multidimensional fitness function to search the global minimal test point set, and an elitist set is used to get more than one possible best solution in the described approach. The efficiency of the proposed method is proven by the same experiments used to verify other methods for optimal test points. Results show that the proposed algorithm in this paper cannot only reduce the computation complexity but also shorten the time consumption. It is particularly useful for large-scale analog circuits.