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.
%0 Journal Article
%1 IJACSA.2014.050101
%A Gamal Abd El-Nasser A. Said Abeer M. Mahmoud, El-Sayed M El-Horbaty
%D 2014
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K (GA); (QAP); (SA); (TS); Algorithm Analysis Annealing Assignment Genetic Performance Problem Quadratic Search Simulated Tabu
%N 1
%T A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem
%U http://ijacsa.thesai.org/
%V 5
%X 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.
@article{IJACSA.2014.050101,
abstract = {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. },
added-at = {2014-02-21T08:00:08.000+0100},
author = {{Gamal Abd El-Nasser A. Said Abeer M. Mahmoud}, El-Sayed M El-Horbaty},
biburl = {https://www.bibsonomy.org/bibtex/2383f3e859f136696939362ba6cc83f51/thesaiorg},
interhash = {1d95b89aebd9717c068eb0bd4685b96d},
intrahash = {383f3e859f136696939362ba6cc83f51},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {(GA); (QAP); (SA); (TS); Algorithm Analysis Annealing Assignment Genetic Performance Problem Quadratic Search Simulated Tabu},
number = 1,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem}},
url = {http://ijacsa.thesai.org/},
volume = 5,
year = 2014
}