A hybrid method integrating an elite genetic algorithm with tabu search for the quadratic assignment problem

被引：43
|
作者：
Zhang, Huizhen [1 ]
Liu, Fan [1 ]
Zhou, Yuyang [1 ]
Zhang, Ziying [2 ]
机构：
[1] Univ Shanghai Sci & Technol, Sch Management, Mail Box 459,516 Jungong Rd, Shanghai 200093, Peoples R China
[2] Shanghai Univ Engn Sci, Sch Mat Engn, Shanghai, Peoples R China
来源：
INFORMATION SCIENCES | 2020年 / 539卷 / 539期
基金：
中国国家自然科学基金;
关键词：
Quadratic assignment problem; Genetic algorithm; Tabu search; Elite; OPTIMIZATION; PERFORMANCE; IMPLEMENTATION; CROSSOVER;
D O I：
10.1016/j.ins.2020.06.036
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The Quadratic Assignment Problem (QAP) is one of the most studied classical combinatorial optimization problems. QAP has many practical applications. Designing enhanced meta-heuristic approaches for the QAP is an active research area. In this work, we propose a hybrid algorithm (EGATS) that combines an elite genetic algorithm and tabu search to solve the QAP. In the optimization process, EGATS employs two kinds of elite crossovers, repeated 2-exchange mutation, and tabu search to strike a balance between exploitation and exploration. We evaluated the performance of EGATS through computational experiments on 135 well-known benchmark instances from the quadratic assignment problem library, QAPLIB. EGATS obtained the best-known solution for 131 instances. Compared to other popular meta-heuristic algorithms in the literature, EGATS is a competitive method for the QAP. (C) 2020 Elsevier Inc. All rights reserved.
引用
收藏
页码：347 / 374
页数：28
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