基于离散混沌蝙蝠算法的测试点选择

针对模拟电路测试点的选择问题,基于智能优化算法的测试点选择方法相比于传统的迭代法具有一定的优越性,但是仍然存在算法复杂度过高等问题.在深入研究蝙蝠算法(BA)的基础上,采用混沌映射对算法进行优化,并用变换函数将算法离散化,提出一种离散混沌蝙蝠算法(CBBA),将算法应用于测试点选择.通过实验仿真,证明该算法具有较高的准确度,而且与其他算法对比发现,该算法具有更好的性能.

Test Point Selection Based on Chaotic Binary Bats Algorithm

According to the selection of test point for analog circuits, test point selection method based on intelligent optimization algorithm has advantage over the traditional iterative method, but the algorithm still exits some problems like high complexity and easy falling into local optimum. Based on the deep study of the bat algorithm (BA) , this paper uses the chaotic map to optimize the algorithm and discretize the algorithm with the transform function, then a chaotic binary bats al-gorithm (CBBA) is proposed. When the algorithm is applied to test point selection, it has high accuracy through experimen-tal simulation, what is more, compared with other algorithm, the algorithm has better performance.