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An improved hybrid genetic algorithm: new results for the quadratic assignment problem

被引：78
|
作者：
Misevicius, A [1 ]
机构：
[1] Kaunas Univ Technol, Dept Pract Informat, LT-3031 Kaunas, Lithuania
来源：
KNOWLEDGE-BASED SYSTEMS | 2004年 / 17卷 / 2-4期
关键词：
genetic algorithms; combinatorial optimization; quadratic assignment problem;
D O I：
10.1016/j.knosys.2004.03.001
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose an improved hybrid genetic algorithm (IHGA). It uses a robust local improvement procedure as well as an effective restart mechanism that is based on so-called 'shift mutations'. IHGA has been applied to the well-known combinatorial optimization problem, the quadratic assignment problem (QAP). The results obtained from the experiments on different QAP instances show that the proposed algorithm appears to be superior to other approaches that are among the best algorithms for the QAP. The high efficiency of our algorithm is also corroborated by the fact that new record-breaking solutions were obtained for a number of large real-life instances. (C) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码：65 / 73
页数：9
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