文章基本信息

标题：A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem
本地全文：下载
作者：Gamal Abd El-Nasser A. Said ; Abeer M. Mahmoud ; El-Sayed M. El-Horbaty 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2014
卷号：5
期号：1
DOI：10.14569/IJACSA.2014.050101
出版社：Science and Information Society (SAI)
摘要：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.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Quadratic Assignment Problem (QAP); Genetic Algorithm (GA); Tabu Search (TS); Simulated Annealing (SA); Performance Analysis