  Quadratic Assignment Problem (QAP) is an NP-hard combinatorial optimization
problem, therefore, solving the QAP requires applying one or more of the
meta-heuristic algorithms. This paper presents a comparative study between
Meta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated
annealing for solving a real-life (QAP) and analyze their performance in terms
of both runtime efficiency and solution quality. The results show that Genetic
Algorithm has a better solution quality while Tabu Search has a faster
execution time in comparison with other Meta-heuristic algorithms for solving
QAP.
