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