The quadratic assignment problem is among the harder combinatorial op- timization problems in that even small instances might be difﬁcult to solve and for different algorithms different instances pose challenges to solve. Research on the quadratic assignment problem has thus focused on developing methods that defy the problem’s variety and that can solve a vast number of instances effectively. The topic of this work is to compare the performance of well-known “standard” meta- heuristics with specialized and adapted metaheuristics and analyze their behavior. Empirical validation of the results is performed on a highly diverse set of instances that are collected and published in form of the quadratic assignment problem library. The data in these instances come from real-world applications on the one hand and from randomly generated sources on the other hand.