A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem
This is an incremental study comparing existing methods for solving a specific NP-hard optimization problem.
This paper tackled the Quadratic Assignment Problem (QAP) by comparing meta-heuristic algorithms, finding that Genetic Algorithm achieved better solution quality and Tabu Search had faster execution times.
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.