Ant Colony Optimization for Solving Multiple Boolean SAT Instances

Ant Colony Optimization (ACO) [2] is a nondeterministic algorithm framework that mimics the foraging behavior of ants to solve difficult optimization problems. Several researchers have successfully applied ant based algorithm framework in different fields of engineering, but never in VLSI testing. In this paper, we first describe the basics of ACO. We then consider the problem of simultaneously solving multiple Boolean SAT instances using a novel ant based algorithm framework as a way to demostrate the use of ACO for researchers in the VLSI test community. Experiments conducted on ISCAS’89 benchmarks demonstrate that ant based algorithm can prove satisfiability of large number of Boolean equations using very short CPU time. We strongly feel that the optimization technique presented in this paper will open the gates for researchers solving several other optimization problems in the testing field such as vector re-ordering, scan chain partitioning, vector compaction, sequential test pattern generation just to name a few.