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Quadratic Assignment Problem (QAP) is an NP-hard combinatorial optimizationproblem, therefore, solving the QAP requires applying one or more of themeta-heuristic algorithms. This paper presents a comparative study betweenMeta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulatedannealing for solving a real-life (QAP) and analyze their performance in termsof both runtime efficiency and solution quality. The results show that GeneticAlgorithm has a better solution quality while Tabu Search has a fasterexecution time in comparison with other Meta-heuristic algorithms for solvingQAP.

A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem