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A quadratic assignment problem (QAP) is a combinatorial optimization problemthat belongs to the class of NP-hard ones. So, it is difficult to solve in thepolynomial time even for small instances. Research on the QAP has thus focusedon obtaining a method to overcome this problem. Heuristics and meta-heuristicsalgorithm are prevalent solution methods for this problem. This paper is one ofcomparative studies to apply different metaheuristic algorithms for solving theQAP. One of the most popular approaches for categorizing meta-heuristicalgorithms is based on a search strategy, including (1) local searchimprovement meta-heuristics and (2) global search-based meta-heuristics. Thematter that distinguishes this paper from the other is the comparativeperformance of local and global search (both EA and SI), in whichmeta-heuristics that consist of genetic algorithm (GA), particle swarmoptimization (PSO), hybrid GA-PSO, grey wolf optimization (GWO), harmony searchalgorithm (HAS) and simulated annealing (SA). Also, one improvement heuristicalgorithm (ie, 2-Opt) is used to compare with others. The PSO, GWO and 2-Optalgorithms are improved to achieve the better comparison toward the otheralgorithms for evaluation. In order to analysis the comparative advantage ofthese algorithms, eight different factors are presented. By taking into accountall these factors, the test is implemented in six test problems of the QAPLibrary (QAPLIB) from different sizes. Another contribution of this paper is tomeasure a strong convergence condition for each algorithm in a new way.

Performance Analysis of Meta-heuristic Algorithms for a Quadratic Assignment Problem