Finding the best performing solution algorithm for QAP

The quadratic assignment problem (QAP) of NP-Hard problems class is known as one of the hardest combinatorial optimization problems. In this thesis, a search is performed on the metaheuristics that have recently found widespread application in order to identify a heuristic procedure that performs well with the QAP. Algorithms which reflect implementations of Simulated Annealing, Genetic Algorithm, Scatter Search and Grasp ? type metaheuristics are tested and using real test problems these algorithms are compared. Same set of algorithms are tested on general QAP problems and observation to identify successful algorithms is made. To conclude the best performing heuristic is not easy to name due to the fact that the performance of a heuristic depends on the context of the problem, which determines the structure and relationships of problem parameters.