Optimization of analog circuits test mode based on Ants Colony Algorithms

In this paper, optimization technique based on Ants Colony Algorithms (ACA) was studied for test node selection. The technique yields high faults detection and isolation rates with less number of nodes. This concept can also be applied in the domain of fault dictionary. In the technique proposed, the objective function consists of the fault detection rate defined as the number of faults detected divided by the total number of faults, and the fault isolation rate which is equal to the number of faults isolated divided by the number of fault detected. The transfer probability, also regarded as the rate of change of positions, is equal to the exponential power of - dij where dij is the difference between two points located in a two dimensional space. The updating procedure measures each solution against the last N solutions globally made by ACA. As soon as N solutions are available, their moving average X is computed; each new solution G is compared with X. If G value is lower than X value, the trail level of the last solution's move is increased, otherwise it's decreased. The efficiency of ACA-based technique was proved theoretically and by means of simulation. The source code proving the suitability of ACA was developed in Matlab. The both fault detection and isolation rates were compared with the rates generated by the AdvancedSCH, a platform developed by our research team. The comparison shows that ACA-based technique best suits for test node selection.