The quadratic assignment problem (QAP) is one of the hardest NPhard problems and problems with a dimension of 20 or more can be difficult to solve using exact methods. The QAP has a set of facilities and a set of locations. The goal is to assign each facility to a location such that the product of the flow between pairs of facilities and the distance between them are minimized. Sometimes there is also a cost associated with assigning a facility to a location. In this work, I solve the QAP using a population based iterative local search with open source code in C++. Results show that the code is able to solve all nug instances to optimality, thereby proving that the algorithm is capable of solving larger problems for which optimum solutions are not known.