A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem
Artificial Intelligence
2014-07-21 v1 Neural and Evolutionary Computing
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.
Keywords
Cite
@article{arxiv.1407.4863,
title = {A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem},
author = {Gamal Abd El-Nasser A. Said and Abeer M. Mahmoud and El-Sayed M. El-Horbaty},
journal= {arXiv preprint arXiv:1407.4863},
year = {2014}
}
Comments
6 pages, 3 figures