In this thesis, we propose to use relevant potential invariants in a simulation-based swarmintelligence-based test generation technique to generate relevant test vectors for design validation at the Register Transfer Level (RTL). Providing useful guidance to the test generator for such techniques is critical. In our approach, we provide guidance by exploiting potential invariants in the design. These potential invariants are obtained using random stimuli such that they are true under these stimuli. Since these potential invariants are only likely to be true, we try to generate stimuli that can falsify them. Any such vectors would help reach some corners of the design. However, the space of potential invariants can be extremely large. To reduce execution time, we also implement a two-layer filter to remove the irrelevant potential invariants that may not contribute in reaching difficult states. With the filter, the vectors generated thus help to reduce the overall test length while still reach the same coverage as considering all unfiltered potential invariants. Experimental results show that with only the filtered potential invariants, we were able to reach equal or better branch coverage than that reported by BEACON in the ITC99 benchmarks, with considerable reduction in vector lengths, at reduced execution time. / Master of Science / Over the recent years, size and complexity of hardware designs are increasing at an enormous rate. Due to this, verification of these designs is of utmost importance and demands much more resources and time than designing of these hardware. To project the information of the designs, developers use Hardware Descriptive Languages (HDL), that includes the important decision points of the system, also called branches of the circuit. There are several methodologies proposed to check how many branches of the design can be traversed by set of inputs. This practice is important to confirm correct functionality of the design as we can catch all the faults in the design at these decision points. Some of these methodologies include checking with random inputs, exhaustively checking for every possible input, investing many hours of labor to verify with appropriate inputs, or simply automating the process of generating inputs. In this thesis, we focus on one such automated process called BEACON or Branch-oriented Evolutionary Ant Colony OptimizatioN. We propose a modification to improve this method by using standard properties of the design. These properties, also known as invariants, help to cover those branches that require extra effort in terms of both inputs and time, and are thus, hard to cover. When we add these significant invariants to the design, modified BEACON is able to cover almost all accessible branches in the system with significantly less amount of time and lesser number of vectors than original BEACON itself, which helps save a lot of resources.