An ant colony optimization technique for abstraction-guided state justification

Abstract

In this paper, a novel heuristic for abstraction-guided state justification is proposed based on ant colony optimization (ACO). A probabilistic state transition model is developed to help formulate the state justification problem as a searching scheme of artificial ants. The amount of pheromone left by the ants is directly proportional to the quality of the search so that it can serve as an effective guidance for the search. In addition, the intelligence based on the collective behavior is capable of avoiding critical dead-end states as well as fast convergence to the target state. Experimental results demonstrated that our approach is superior in reaching hard-to-reach states in sequential circuit compared to other methods.