基于混合遗传蚁群算法的数字微流控芯片测试路径规划

数字微流控芯片在生化检测领域的应用越来越广泛,为保障芯片的可靠性必须对其进行全面且高效的故障测试。随着芯片规模的扩大,故障测试问题也越来越复杂。针对数字微流控芯片的灾难性故障测试,为提高故障测试方法的时间效率,本文提出了一种基于混合遗传蚁群算法的测试路径规划方案。首先,该方案优化了芯片故障测试模型的转化过程;其次,先利用遗传算法的全局特性生成全局较优测试路径,并根据较优测试路径形成蚁群算法的初始信息素分布;最后,再利用蚁群算法搜索最优测试路径。该方案适用于离线测试和在线测试,能够兼容规则和非规则芯片。实验仿真结果表明,该方案提高了测试模型转化的效率,在获得较优测试路径的同时改善了测试算法的收敛特性,提高了测试方法的时间效率。

Test path scheduling of digital microfluidic biochips based on combined genetic and ant colony algorithm

As digital microfluidic biochip is widely applied in biochemical detection fields,it is required to test the biochips completely and efficiently to guarantee the reliability of biochip.With the expansion of the size of biochip,the fault testing problem of digital microfluidic biochip is getting more and more complex.Aiming at the catastrophic faults of biochip,a test path scheduling based on combined genetic and colony algorithm is proposed to improve time efficiency of testing method.Firstly,the scheduling optimizes the conversion process of fault testing model.Then,some global excellent test paths are generated by using the global property of genetic algorithm,and the initial pheromone distribution of ant colony algorithm is formed according to these excellent test paths.Finally,the optimal test paths are searched by using ant colony algorithm.This scheduling is suitable for off-line and on-line testing,and it can also be used for rectangle and non-rectangular biochip.The experiment results show that this scheduling can improve the efficiency of the conversion process of fault testing model.At the same time,this scheduling can improve the astringency and the time efficiency of testing algorithm in the case of getting optimized testing paths.